Virtual reality environment based manipulation of multi-layered multi-view interactive digital media representations

ABSTRACT

Various embodiments of the present disclosure relate generally to systems and methods for generating multi-view interactive digital media representations in a virtual reality environment. According to particular embodiments, a plurality of images is fused into a first content model and a first context model, both of which include multi-view interactive digital media representations of objects. Next, a virtual reality environment is generated using the first content model and the first context model. The virtual reality environment includes a first layer and a second layer. The user can navigate through and within the virtual reality environment to switch between multiple viewpoints of the content model via corresponding physical movements. The first layer includes the first content model and the second layer includes a second content model and wherein selection of the first layer provides access to the second layer with the second content model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. application Ser.No. 16/726,090, filed on Dec. 23, 2019, which is a continuation of U.S.application Ser. No. 15/724,081, filed on Oct. 3, 2017, now U.S. Pat.No. 10,514,820, issued on Dec. 24, 2019, which is a continuation of U.S.application Ser. No. 15/682,362, filed on Aug. 21, 2017, now U.S. Pat.No. 10,222,932, issued on Mar. 5, 2019. In addition, this applicationclaims the benefit of U.S. Provisional Application No. 62/377,519, filedon Aug. 19, 2016, which is incorporated by reference herein in itsentirety for all purposes. In addition, this application claims thebenefit of U.S. Provisional Application No. 62/377,517, filed on Aug.19, 2016, which is incorporated by reference herein in its entirety forall purposes. In addition, this application claims the benefit of U.S.Provisional Application No. 62/377,513, filed on Aug. 19, 2016, which isincorporated by reference herein in its entirety for all purposes. Inaddition, this application is a Continuation-in-Part of U.S. applicationSer. No. 14/800,638, filed on Jul. 15, 2015, now U.S. Pat. No.9,940,541, issued on Apr. 10, 2018, which is also incorporated byreference herein in its entirety for all purposes.

TECHNICAL FIELD

The present disclosure relates to layers in surround views, whichincludes providing a multi-view interactive digital media representation(MIDMR).

DESCRIPTION OF RELATED ART

With modern computing platforms and technologies shifting towards mobileand wearable devices that include camera sensors as native acquisitioninput streams, the desire to record and preserve moments digitally in adifferent form than more traditional two-dimensional (2D) flat imagesand videos has become more apparent. Traditional digital media formatstypically limit their viewers to a passive experience. For instance, a2D flat image can be viewed from one angle and is limited to zooming inand out. Accordingly, traditional digital media formats, such as 2D flatimages, do not easily lend themselves to reproducing memories and eventswith high fidelity.

Current predictions (Ref: KPCB “Internet Trends 2012” presentation”)indicate that every several years the quantity of visual data that isbeing captured digitally online will double. As this quantity of visualdata increases, so does the need for much more comprehensive search andindexing mechanisms than ones currently available. Unfortunately,neither 2D images nor 2D videos have been designed for these purposes.Accordingly, improved mechanisms that allow users to view and indexvisual data, as well as query and quickly receive meaningful resultsfrom visual data are desirable.

In addition, virtual reality has become increasingly popular. Withvirtual reality (VR) technology, a user can experience an immersivedigital world by engaging with virtual reality equipment. However, withstandard VR technology, the digital worlds are usually limited tomanufactured computer animated environments, such as simulators. Suchcomputer animation is not “realistic” to a real world environment. Evenif a VR system does attempt to simulate the real world environment, suchsystems are often limited to using three dimensional polygon modelingwith subsequent texture rendering. Such VR systems do not seem“realistic” to a user and usually require multiple processing steps forgenerating the three dimensional models. Thus, there exists a need forimproved VR systems that provide a more “realistic” feel to a user,while reducing the amount of processing needed to generate realisticthree-dimensional objects in a virtual reality environment.

Overview

According to various embodiments, a multi-view interactive digital media(MIDM) is used herein to describe any one of various images (or othermedia data) used to represent a dynamic surrounding view of an object ofinterest and/or contextual background. Such dynamic surrounding view maybe referred to herein as multi-view interactive digital mediarepresentation (MIDMR). Various embodiments of the present disclosurerelate generally to systems and methods for generating multi-viewinteractive digital media representations in a virtual realityenvironment. According to particular embodiments, a plurality of imagesis fused into a first content model and a first context model, both ofwhich include multi-view interactive digital media representations ofobjects. Next, a virtual reality environment is generated using thefirst content model and the first context model. The virtual realityenvironment includes a first layer and a second layer. The user cannavigate through and within the virtual reality environment to switchbetween multiple viewpoints of the content model via correspondingphysical movements. The first layer includes the first content model andthe second layer includes a second content model and wherein selectionof the first layer provides access to the second layer with the secondcontent model.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular embodiments of the present disclosure.

FIG. 1 illustrates an example of a surround view acquisition system.

FIG. 2 illustrates an example of a process flow for generating asurround view.

FIG. 3 illustrates one example of multiple camera views that can befused into a three-dimensional (3D) model to create an immersiveexperience.

FIG. 4A illustrates one example of separation of content and context ina surround view.

FIG. 4B illustrates one example of layering in a surround view.

FIG. 4C illustrates one example of a process for modifying a layer in asurround view.

FIGS. 5A-5B illustrate examples of concave view and convex views,respectively, where both views use a back-camera capture style.

FIGS. 6A-6E illustrate examples of various capture modes for surroundviews.

FIG. 7A illustrates one example of a process for recording data that canbe used to generate a surround view.

FIG. 7B illustrates one example of a dynamic panorama capture process.

FIG. 7C illustrates one example of a dynamic panorama capture processwhere the capture device is rotated through the axis of rotation.

FIG. 7D illustrates one example of a dynamic panorama with dynamiccontent.

FIG. 7E illustrates one example of capturing a dynamic panorama with a3D effect.

FIG. 7F illustrates one example of a dynamic panorama with parallaxeffect.

FIG. 7G illustrates one example of an object panorama capture process.

FIG. 7H illustrates one example of a background panorama with an objectpanorama projected on it.

FIG. 7I illustrates one example of multiple objects constituting anobject panorama.

FIG. 7J illustrates one example of changing the viewing angle of anobject panorama based on user navigation.

FIG. 7K illustrates one example of a selfie panorama capture process.

FIG. 7L illustrates one example of a background panorama with a selfiepanorama projected on it.

FIG. 7M illustrates one example of extended views of panoramas based onuser navigation.

FIG. 8 illustrates an example of a surround view in whichthree-dimensional content is blended with a two-dimensional panoramiccontext.

FIG. 9 illustrates one example of a space-time surround view beingsimultaneously recorded by independent observers.

FIG. 10 illustrates one example of separation of a complex surround-viewinto smaller, linear parts.

FIG. 11 illustrates one example of a combination of multiple surroundviews into a multi-surround view.

FIG. 12 illustrates one example of a process for prompting a user foradditional views of an object of interest to provide a more accuratesurround view.

FIGS. 13A-13B illustrate an example of prompting a user for additionalviews of an object to be searched.

FIG. 14 illustrates one example of a process for navigating a surroundview.

FIG. 15 illustrates an example of swipe-based navigation of a surroundview.

FIG. 16A illustrates examples of a sharing service for surround views,as shown on a mobile device and browser.

FIG. 16B illustrates examples of surround view-related notifications ona mobile device.

FIG. 17A illustrates one example of a process for providing objectsegmentation.

FIG. 17B illustrates one example of a segmented object viewed fromdifferent angles.

FIG. 18 illustrates one example of various data sources that can be usedfor surround view generation and various applications that can be usedwith a surround view.

FIG. 19 illustrates one example of a process for providing visual searchof an object, where the search query includes a surround view of theobject and the data searched includes three-dimensional models.

FIG. 20 illustrates one example of a process for providing visual searchof an object, where the search query includes a surround view of theobject and the data searched includes two-dimensional images.

FIG. 21 illustrates an example of a visual search process.

FIG. 22 illustrates an example of a process for providing visual searchof an object, where the search query includes a two-dimensional view ofthe object and the data searched includes surround view(s).

FIG. 23 illustrates a particular example of a computer system that canbe used with various embodiments of the present disclosure.

FIGS. 24A-C illustrate example screenshots of a virtual realityenvironment from different angles, in accordance with variousembodiments of the present disclosure.

FIGS. 25A-G illustrate example screenshots of a virtual realityenvironment with content model manipulation, in accordance with variousembodiments of the present disclosure.

FIGS. 26A-M illustrate example screenshots of a virtual realityenvironment with multiple interactive layers, in accordance with variousembodiments of the present disclosure.

FIG. 27 illustrates an example of a method for infinite smoothingbetween image frames, in accordance with one or more embodiments.

FIG. 28 illustrates an example method for generating stereo pairs forvirtual reality or augmented reality using a single lens camera, inaccordance with one or more embodiments.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examples of thedisclosure including the best modes contemplated by the inventors forcarrying out the disclosure. Examples of these specific embodiments areillustrated in the accompanying drawings. While the present disclosureis described in conjunction with these specific embodiments, it will beunderstood that it is not intended to limit the disclosure to thedescribed embodiments. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the disclosure as defined by the appendedclaims.

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the present disclosure.Particular embodiments of the present disclosure may be implementedwithout some or all of these specific details. In other instances, wellknown process operations have not been described in detail in order notto unnecessarily obscure the present disclosure.

In various embodiments, the virtual reality system uses real images togenerate three dimensional objects in a virtual environment. Surroundviews of objects of interest are generated by fusing actual images ofthe objects of interest. In some embodiments, the system does not use anintermediate polygon model generation step. Instead, objects areidentified within the plurality of images. Next, common objects from theplurality of images are identified and different views/angles of eachcommon object are stored. Next, measurements and dimensions of thecommon object are extracted from the different views/angles of thecommon object. In some embodiments, the three dimensional measurementsof an object are extracted by comparing differences of common featuresof the common object in different surround views. After extracting themeasurements and dimensions of the object, a three dimensional contentmodel of the object is generated by stitching together various images ofthe object. The various images correspond to different angles and viewsof the object obtained via concave or convex movement of an imagecapturing device, e.g. a camera. B y directly using the images, thevirtual reality system conserves processing resources and time. Inaddition, the image generated content model is more accurate thantraditional polygon generation systems that estimate an object'sdimensions.

In some embodiments, the content model is three-dimensional and allows auser in the virtual reality environment to navigate around the object bycircling the object in the virtual reality environment. In someembodiments, the virtual reality environment is mapped to the physicalworld. For instance, a virtual room with objects can be mapped to aphysical 20 ft×20 ft room, such that when a user puts on the virtualreality headset or goggles, or any other type of engagement device, theuser can see and interact with digital objects that are in the virtualroom but are not physically present in the real world room. For example,an object such as a painting can be “fixed” to the north wall of theroom such that whenever the user is physically oriented toward the northwall, the user can see the painting. The painting can be any paintingcaptured through a camera. For example, the famous painting “the MonaLisa” can be captured by a moving camera (thus capturing the Mona Lisaat different angles) through space. From all the different imagesgenerated, the dimensions of the Mona Lisa can be captured. The MonaLisa can then be replicated by generating a three dimensional model ofthe painting by stitching together the different images. The threedimensional model can then be “hung” on the north wall of the room suchthat any user engaged in the virtual reality environment (as usedherein, “an engaged user”) can “see” the Mona Lisa when looking at thenorth wall of the room.

In some embodiments, the objects are “fixed in space” and correspond toactual physical dimensions of a room. For example, a three dimensionalmodel of a chair can be “placed” in the center of the room such that anengaged user can walk up to the model of the chair by ambulating to thecenter of the room. In some embodiments, in order to simulate realisticobjects, the size of the object model (or appearance of size) increasesas a user moves toward the object model and decreases as the user movesaway from the object model. In some embodiments, the object models arefixed in space to an actual physical location. Thus, in suchembodiments, no matter where the user starts, the object models alwaysstart in the same location. For example, in the room scenario above, ifthe chair was fixed to the center of the room, then the chair willalways be in the center of the room no matter if the user starts in thecenter or starts at the wall. In other embodiments, the object modelsare not fixed to physical locations, but rather are fixed to relativepositions to the user. In such embodiments, the objects always start ata predetermined distance relative to the start point of the user. As theuser moves toward the object, the object appears bigger, and vice versa.For example, if the chair always starts ten feet away from the user, thechair starts in the center of the room if the user starts at the wall.Similarly, the chair then also starts at the wall if the user starts inthe center of the room. As used herein, “starts” refers to the initiallocation of an object when the user turns on or engages with the virtualreality environment. In relative location embodiments, the placement ofobjects is more flexible and can be easily altered. For example, thesettings for the VR system can be set such that the chair always starts10 feet away from the user or adjusted to 5 feet or 15 feet. Of coursewith relative location embodiments, the system still has to take intoaccount real world road blocks or obstructions and adjust accordingly.For example, if the chair is set to start at 25 feet from a user and theuser is within a 20 ft×20 ft room, then if the user starts in the centerof the room the chair would have to start beyond the wall. In suchcases, the system can auto-adjust such that the object must appear atthe farthest distance to the user but before the wall, or the system canchoose to eliminate the chair from the VR environment altogether.

In some embodiments, objects are automatically identified and extractedusing similarity algorithms for recognizing objects common to aplurality of pictures. In some embodiments, objects are automaticallyidentified and extracted from the plurality of images and stored to useas content models for various VR environments. In some embodiments,actual dimensions of objects are calculated by comparing the dimensionsto known objects or objects with known dimensions in the images. Forexample, if the object is captured by a camera (as used herein,“captured by a camera” refers to obtaining a plurality of images usingan image capturing device such as a camera) in a known setting such asshow room with objects of known dimensions, or if an object is capturedwhile being next to an object of a standard or known size, such as acredit card or a ruler, then the real world dimensions of the object canalso be determined. However, if an object is captured in an environmentwith no known objects or objects with known dimensions, real worlddimensions can still be estimated by identifying objects that aresimilar in size to either the object or to objects in the background. Insome embodiments, the VR environment includes a context model, e.g.scenery, in addition to content models, e.g. objects. For example in theroom example above, a content model could be the chair located in thecenter of the room. The context model could then be aquariums, trees,jail bars, and other scenery replacements for the walls of the room. Insome embodiments, the context model is the real world scenerysurrounding the object when the object was captured in a plurality ofimages by the camera.

Various aspects of the present disclosure relate generally to systemsand methods for analyzing the spatial relationship between multipleimages and video together with location information data, for thepurpose of creating a single representation, a surround view, whicheliminates redundancy in the data, and presents a user with aninteractive and immersive active viewing experience. According tovarious embodiments, active is described in the context of providing auser with the ability to control the viewpoint of the visual informationdisplayed on a screen. In particular example embodiments, the surroundview data structure (and associated algorithms) is natively built for,but not limited to, applications involving visual search.

According to various embodiments of the present disclosure, a surroundview is a multi-view interactive digital media representation. Withreference to FIG. 1 , shown is one example of a surround viewacquisition system 100. In the present example embodiment, the surroundview acquisition system 100 is depicted in a flow sequence that can beused to generate a surround view. According to various embodiments, thedata used to generate a surround view can come from a variety ofsources. In particular, data such as, but not limited to two-dimensional(2D) images 104 can be used to generate a surround view. These 2D imagescan include color image data streams such as multiple image sequences,video data, etc., or multiple images in any of various formats forimages, depending on the application. Another source of data that can beused to generate a surround view includes location information 106. Thislocation information 106 can be obtained from sources such asaccelerometers, gyroscopes, magnetometers, GPS, WiFi, IMU-like systems(Inertial Measurement Unit systems), and the like. Yet another source ofdata that can be used to generate a surround view can include depthimages 108. These depth images can include depth, 3D, or disparity imagedata streams, and the like, and can be captured by devices such as, butnot limited to, stereo cameras, time-of-flight cameras,three-dimensional cameras, and the like.

In the present example embodiment, the data can then be fused togetherat sensor fusion block 110. In some embodiments, a surround view can begenerated a combination of data that includes both 2D images 104 andlocation information 106, without any depth images 108 provided. Inother embodiments, depth images 108 and location information 106 can beused together at sensor fusion block 110. Various combinations of imagedata can be used with location information at 106, depending on theapplication and available data.

In the present example embodiment, the data that has been fused togetherat sensor fusion block 110 is then used for content modeling 112 andcontext modeling 114. As described in more detail with regard to FIG. 4, the subject matter featured in the images can be separated intocontent and context. The content can be delineated as the object ofinterest and the context can be delineated as the scenery surroundingthe object of interest. According to various embodiments, the contentcan be a three-dimensional model, depicting an object of interest,although the content can be a two-dimensional image in some embodiments,as described in more detail below with regard to FIG. 4 . Furthermore,in some embodiments, the context can be a two-dimensional modeldepicting the scenery surrounding the object of interest. Although inmany examples the context can provide two-dimensional views of thescenery surrounding the object of interest, the context can also includethree-dimensional aspects in some embodiments. For instance, the contextcan be depicted as a “flat” image along a cylindrical “canvas,” suchthat the “flat” image appears on the surface of a cylinder. In addition,some examples may include three-dimensional context models, such as whensome objects are identified in the surrounding scenery asthree-dimensional objects. According to various embodiments, the modelsprovided by content modeling 112 and context modeling 114 can begenerated by combining the image and location information data, asdescribed in more detail with regard to FIG. 3 .

According to various embodiments, context and content of a surround vieware determined based on a specified object of interest. In someexamples, an object of interest is automatically chosen based onprocessing of the image and location information data. For instance, ifa dominant object is detected in a series of images, this object can beselected as the content. In other examples, a user specified target 102can be chosen, as shown in FIG. 1 . It should be noted, however, that asurround view can be generated without a user specified target in someapplications.

In the present example embodiment, one or more enhancement algorithmscan be applied at enhancement algorithm(s) block 116. In particularexample embodiments, various algorithms can be employed during captureof surround view data, regardless of the type of capture mode employed.These algorithms can be used to enhance the user experience. Forinstance, automatic frame selection, stabilization, view interpolation,filters, and/or compression can be used during capture of surround viewdata. In some examples, these enhancement algorithms can be applied toimage data after acquisition of the data. In other examples, theseenhancement algorithms can be applied to image data during capture ofsurround view data.

According to particular example embodiments, automatic frame selectioncan be used to create a more enjoyable surround view. Specifically,frames are automatically selected so that the transition between themwill be smoother or more even. This automatic frame selection canincorporate blur- and overexposure-detection in some applications, aswell as more uniformly sampling poses such that they are more evenlydistributed.

In some example embodiments, stabilization can be used for a surroundview in a manner similar to that used for video. In particular,keyframes in a surround view can be stabilized for to produceimprovements such as smoother transitions, improved/enhanced focus onthe content, etc. However, unlike video, there are many additionalsources of stabilization for a surround view, such as by using IMUinformation, depth information, computer vision techniques, directselection of an area to be stabilized, face detection, and the like.

For instance, IMU information can be very helpful for stabilization. Inparticular, IMU information provides an estimate, although sometimes arough or noisy estimate, of the camera tremor that may occur duringimage capture. This estimate can be used to remove, cancel, and/orreduce the effects of such camera tremor.

In some examples, depth information, if available, can be used toprovide stabilization for a surround view. Because points of interest ina surround view are three-dimensional, rather than two-dimensional,these points of interest are more constrained and tracking/matching ofthese points is simplified as the search space reduces. Furthermore,descriptors for points of interest can use both color and depthinformation and therefore, become more discriminative. In addition,automatic or semi-automatic content selection can be easier to providewith depth information. For instance, when a user selects a particularpixel of an image, this selection can be expanded to fill the entiresurface that touches it. Furthermore, content can also be selectedautomatically by using a foreground/background differentiation based ondepth. In various examples, the content can stay relativelystable/visible even when the context changes.

According to various examples, computer vision techniques can also beused to provide stabilization for surround views. For instance,keypoints can be detected and tracked. However, in certain scenes, suchas a dynamic scene or static scene with parallax, no simple warp existsthat can stabilize everything. Consequently, there is a trade-off inwhich certain aspects of the scene receive more attention tostabilization and other aspects of the scene receive less attention.Because a surround view is often focused on a particular object ofinterest, a surround view can be content-weighted so that the object ofinterest is maximally stabilized in some examples.

Another way to improve stabilization in a surround view includes directselection of a region of a screen. For instance, if a user taps to focuson a region of a screen, then records a convex surround view, the areathat was tapped can be maximally stabilized. This allows stabilizationalgorithms to be focused on a particular area or object of interest.

In some examples, face detection can be used to provide stabilization.For instance, when recording with a front-facing camera, it is oftenlikely that the user is the object of interest in the scene. Thus, facedetection can be used to weight stabilization about that region. Whenface detection is precise enough, facial features themselves (such aseyes, nose, mouth) can be used as areas to stabilize, rather than usinggeneric keypoints.

According to various examples, view interpolation can be used to improvethe viewing experience. In particular, to avoid sudden “jumps” betweenstabilized frames, synthetic, intermediate views can be rendered on thefly. This can be informed by content-weighted keypoint tracks and IMUinformation as described above, as well as by denser pixel-to-pixelmatches. If depth information is available, fewer artifacts resultingfrom mismatched pixels may occur, thereby simplifying the process. Asdescribed above, view interpolation can be applied during capture of asurround view in some embodiments. In other embodiments, viewinterpolation can be applied during surround view generation.

In some examples, filters can also be used during capture or generationof a surround view to enhance the viewing experience. Just as manypopular photo sharing services provide aesthetic filters that can beapplied to static, two-dimensional images, aesthetic filters cansimilarly be applied to surround images. However, because a surroundview representation is more expressive than a two-dimensional image, andthree-dimensional information is available in a surround view, thesefilters can be extended to include effects that are ill-defined in twodimensional photos. For instance, in a surround view, motion blur can beadded to the background (i.e. context) while the content remains crisp.In another example, a drop-shadow can be added to the object of interestin a surround view.

In various examples, compression can also be used as an enhancementalgorithm 116. In particular, compression can be used to enhanceuser-experience by reducing data upload and download costs. Becausesurround views use spatial information, far less data can be sent for asurround view than a typical video, while maintaining desired qualitiesof the surround view. Specifically, the IMU, keypoint tracks, and userinput, combined with the view interpolation described above, can allreduce the amount of data that must be transferred to and from a deviceduring upload or download of a surround view. For instance, if an objectof interest can be properly identified, a variable compression style canbe chosen for the content and context. This variable compression stylecan include lower quality resolution for background information (i.e.context) and higher quality resolution for foreground information (i.e.content) in some examples. In such examples, the amount of datatransmitted can be reduced by sacrificing some of the context quality,while maintaining a desired level of quality for the content.

In the present embodiment, a surround view 118 is generated after anyenhancement algorithms are applied. The surround view can provide amulti-view interactive digital media representation. In variousexamples, the surround view can include three-dimensional model of thecontent and a two-dimensional model of the context. However, in someexamples, the context can represent a “flat” view of the scenery orbackground as projected along a surface, such as a cylindrical orother-shaped surface, such that the context is not purelytwo-dimensional. In yet other examples, the context can includethree-dimensional aspects.

According to various embodiments, surround views provide numerousadvantages over traditional two-dimensional images or videos. Some ofthese advantages include: the ability to cope with moving scenery, amoving acquisition device, or both; the ability to model parts of thescene in three-dimensions; the ability to remove unnecessary, redundantinformation and reduce the memory footprint of the output dataset; theability to distinguish between content and context; the ability to usethe distinction between content and context for improvements in theuser-experience; the ability to use the distinction between content andcontext for improvements in memory footprint (an example would be highquality compression of content and low quality compression of context);the ability to associate special feature descriptors with surround viewsthat allow the surround views to be indexed with a high degree ofefficiency and accuracy; and the ability of the user to interact andchange the viewpoint of the surround view. In particular exampleembodiments, the characteristics described above can be incorporatednatively in the surround view representation, and provide the capabilityfor use in various applications. For instance, surround views can beused to enhance various fields such as e-commerce, visual search, 3Dprinting, file sharing, user interaction, and entertainment.

According to various example embodiments, once a surround view 118 isgenerated, user feedback for acquisition 120 of additional image datacan be provided. In particular, if a surround view is determined to needadditional views to provide a more accurate model of the content orcontext, a user may be prompted to provide additional views. Once theseadditional views are received by the surround view acquisition system100, these additional views can be processed by the system 100 andincorporated into the surround view.

With reference to FIG. 2 , shown is an example of a process flow diagramfor generating a surround view 200. In the present example, a pluralityof images is obtained at 202. According to various embodiments, theplurality of images can include two-dimensional (2D) images or datastreams. These 2D images can include location information that can beused to generate a surround view. In some embodiments, the plurality ofimages can include depth images 108, as also described above with regardto FIG. 1 . The depth images can also include location information invarious examples.

According to various embodiments, the plurality of images obtained at202 can include a variety of sources and characteristics. For instance,the plurality of images can be obtained from a plurality of users. Theseimages can be a collection of images gathered from the internet fromdifferent users of the same event, such as 2D images or video obtainedat a concert, etc. In some examples, the plurality of images can includeimages with different temporal information. In particular, the imagescan be taken at different times of the same object of interest. Forinstance, multiple images of a particular statue can be obtained atdifferent times of day, different seasons, etc. In other examples, theplurality of images can represent moving objects. For instance, theimages may include an object of interest moving through scenery, such asa vehicle traveling along a road or a plane traveling through the sky.In other instances, the images may include an object of interest that isalso moving, such as a person dancing, running, twirling, etc.

In the present example embodiment, the plurality of images is fused intocontent and context models at 204. According to various embodiments, thesubject matter featured in the images can be separated into content andcontext. The content can be delineated as the object of interest and thecontext can be delineated as the scenery surrounding the object ofinterest. According to various embodiments, the content can be athree-dimensional model, depicting an object of interest, and thecontent can be a two-dimensional image in some embodiments.

According to the present example embodiment, one or more enhancementalgorithms can be applied to the content and context models at 206.These algorithms can be used to enhance the user experience. Forinstance, enhancement algorithms such as automatic frame selection,stabilization, view interpolation, filters, and/or compression can beused. In some examples, these enhancement algorithms can be applied toimage data during capture of the images. In other examples, theseenhancement algorithms can be applied to image data after acquisition ofthe data.

In the present embodiment, a surround view is generated from the contentand context models at 208. The surround view can provide a multi-viewinteractive digital media representation. In various examples, thesurround view can include a three-dimensional model of the content and atwo-dimensional model of the context. According to various embodiments,depending on the mode of capture and the viewpoints of the images, thesurround view model can include certain characteristics. For instance,some examples of different styles of surround views include a locallyconcave surround view, a locally convex surround view, and a locallyflat surround view. However, it should be noted that surround views caninclude combinations of views and characteristics, depending on theapplication.

With reference to FIG. 3 , shown is one example of multiple camera viewsthat can be fused together into a three-dimensional (3D) model to createan immersive experience. According to various embodiments, multipleimages can be captured from various viewpoints and fused together toprovide a surround view. In the present example embodiment, threecameras 312, 314, and 316 are positioned at locations 322, 324, and 326,respectively, in proximity to an object of interest 308. Scenery cansurround the object of interest 308 such as object 310. Views 302, 304,and 306 from their respective cameras 312, 314, and 316 includeoverlapping subject matter. Specifically, each view 302, 304, and 306includes the object of interest 308 and varying degrees of visibility ofthe scenery surrounding the object 310. For instance, view 302 includesa view of the object of interest 308 in front of the cylinder that ispart of the scenery surrounding the object 310. View 306 shows theobject of interest 308 to one side of the cylinder, and view 304 showsthe object of interest without any view of the cylinder.

In the present example embodiment, the various views 302, 304, and 316along with their associated locations 322, 324, and 326, respectively,provide a rich source of information about object of interest 308 andthe surrounding context that can be used to produce a surround view. Forinstance, when analyzed together, the various views 302, 304, and 326provide information about different sides of the object of interest andthe relationship between the object of interest and the scenery.According to various embodiments, this information can be used to parseout the object of interest 308 into content and the scenery as thecontext. Furthermore, as also described above with regard to FIGS. 1 and2 , various algorithms can be applied to images produced by theseviewpoints to create an immersive, interactive experience when viewing asurround view.

FIG. 4A illustrates one example of separation of content and context ina surround view. According to various embodiments of the presentdisclosure, a surround view is a multi-view interactive digital mediarepresentation of a scene 400. With reference to FIG. 4A, shown is auser 402 located in a scene 400. The user 402 is capturing images of anobject of interest, such as a statue. The images captured by the userconstitute digital visual data that can be used to generate a surroundview.

According to various embodiments of the present disclosure, the digitalvisual data included in a surround view can be, semantically and/orpractically, separated into content 404 and context 406. According toparticular embodiments, content 404 can include the object(s),person(s), or scene(s) of interest while the context 406 represents theremaining elements of the scene surrounding the content 404. In someexamples, a surround view may represent the content 404 asthree-dimensional data, and the context 406 as a two-dimensionalpanoramic background. In other examples, a surround view may representboth the content 404 and context 406 as two-dimensional panoramicscenes. In yet other examples, content 404 and context 406 may includethree-dimensional components or aspects. In particular embodiments, theway that the surround view depicts content 404 and context 406 dependson the capture mode used to acquire the images.

In some examples, such as but not limited to: recordings of objects,persons, or parts of objects or persons, where only the object, person,or parts of them are visible, recordings of large flat areas, andrecordings of scenes where the data captured appears to be at infinity(i.e., there are no subjects close to the camera), the content 404 andthe context 406 may be the same. In these examples, the surround viewproduced may have some characteristics that are similar to other typesof digital media such as panoramas. However, according to variousembodiments, surround views include additional features that distinguishthem from these existing types of digital media. For instance, asurround view can represent moving data. Additionally, a surround viewis not limited to a specific cylindrical, spherical or translationalmovement. Various motions can be used to capture image data with acamera or other capture device. Furthermore, unlike a stitched panorama,a surround view can display different sides of the same object.

Although a surround view can be separated into content and context insome applications, a surround view can also be separated into layers inother applications. With reference to FIG. 4B, shown is one example oflayering in a surround view. In this example, a layered surround view410 is segmented into different layers 418, 420, and 422. Each layer418, 420, and 422 can include an object (or a set of objects), people,dynamic scene elements, background, etc. Furthermore, each of theselayers 418, 420, and 422 can be assigned a depth.

According to various embodiments, the different layers 418, 420, and 422can be displayed in different ways. For instance, different filters(e.g. gray scale filter, blurring, etc.) can be applied to some layersbut not to others. In other examples, different layers can be moved atdifferent speeds relative to each other, such that when a user swipesthrough a surround view a better three-dimensional effect is provided.Similarly, when a user swipes along the parallax direction, the layerscan be displaced differently to provide a better three-dimensionaleffect. In addition, one or more layers can be omitted when displaying asurround view, such that unwanted objects, etc. can be removed from asurround view.

In the present example, a user 412 is shown holding a capture device414. The user 412 moves the capture device 414 along capture motion 416.When the images captured are used to generate a surround view, layers418, 420, and 422 are separated based on depth. These layers can then beprocessed or viewed differently in a surround view, depending on theapplication.

With reference to FIG. 4C, shown is one example of a process forgenerating a surround view with a modified layer in a surround view 430.In particular, a first surround view having a first layer and a secondlayer is obtained at 432. As described above with regard to FIG. 4B, asurround view can be divided into different layers. In the presentexample, the first layer includes a first depth and the second layerincludes a second depth.

Next, the first layer is selected at 434. According to various examples,selecting the first layer includes selecting data within the firstdepth. More specifically, selecting data within the first depth includesselecting the visual data located within the first depth. According tovarious embodiments, the first layer can include features such as anobject, person, dynamic scene elements, background, etc. In someexamples, selection of the first layer is performed automaticallywithout user input. In other examples, selection of the first layer isperformed semi-automatically using user-guided interaction.

After the first layer is selected, an effect is applied to the firstlayer within the first surround view to produce a modified first layerat 436. In one example, the effect applied can be a filter such as ablurring filter, gray scale filter, etc. In another example, the effectapplied can include moving the first layer at a first speed relative tothe second layer, which is moved at a second speed. When the first speedis different from the second speed, three-dimensional effects can beimproved in some instances. In some applications, a parallax effect canoccur, thereby creating a three-dimensional effect.

Next, a second surround view is generated that includes the modifiedfirst layer and the second layer at 438. As described above, applyingone or more effects to the first layer can improve the three-dimensionaleffects of a surround view in some applications. In these applications,the second surround view can have improved three-dimensional effectswhen compared to the first surround view. Other effects can be appliedin different examples, and can emphasize or deemphasize various aspectsof a first surround view to yield a second surround view. In addition,in some applications, a layer can be omitted in a second surround view.Specifically, when the first surround view includes a third layer, thesecond surround view omits this third layer. In one example, this thirdlayer could include an object or person that would be “edited out” inthe generated second surround view. In another example, this third layercould include a background or background elements, and the secondsurround view generated would not include the background or backgroundelements. Of course, any object or feature can be located in thisomitted third layer, depending on the application.

FIGS. 5A-5B illustrate examples of concave and convex views,respectively, where both views use a back-camera capture style. Inparticular, if a camera phone is used, these views use the camera on theback of the phone, facing away from the user. In particular embodiments,concave and convex views can affect how the content and context aredesignated in a surround view.

With reference to FIG. 5A, shown is one example of a concave view 500 inwhich a user is standing along a vertical axis 508. In this example, theuser is holding a camera, such that camera location 502 does not leaveaxis 508 during image capture. However, as the user pivots about axis508, the camera captures a panoramic view of the scene around the user,forming a concave view. In this embodiment, the object of interest 504and the distant scenery 506 are all viewed similarly because of the wayin which the images are captured. In this example, all objects in theconcave view appear at infinity, so the content is equal to the contextaccording to this view.

With reference to FIG. 5B, shown is one example of a convex view 520 inwhich a user changes position when capturing images of an object ofinterest 524. In this example, the user moves around the object ofinterest 524, taking pictures from different sides of the object ofinterest from camera locations 528, 530, and 532. Each of the imagesobtained includes a view of the object of interest, and a background ofthe distant scenery 526. In the present example, the object of interest524 represents the content, and the distant scenery 526 represents thecontext in this convex view.

FIGS. 6A-6E illustrate examples of various capture modes for surroundviews. Although various motions can be used to capture a surround viewand are not constrained to any particular type of motion, three generaltypes of motion can be used to capture particular features or viewsdescribed in conjunction surround views. These three types of motion,respectively, can yield a locally concave surround view, a locallyconvex surround view, and a locally flat surround view. In someexamples, a surround view can include various types of motions withinthe same surround view.

With reference to FIG. 6A, shown is an example of a back-facing, concavesurround view being captured. According to various embodiments, alocally concave surround view is one in which the viewing angles of thecamera or other capture device diverge. In one dimension this can belikened to the motion required to capture a spherical 360 panorama (purerotation), although the motion can be generalized to any curved sweepingmotion in which the view faces outward. In the present example, theexperience is that of a stationary viewer looking out at a (possiblydynamic) context.

In the present example embodiment, a user 602 is using a back-facingcamera 606 to capture images towards world 600, and away from user 602.As described in various examples, a back-facing camera refers to adevice with a camera that faces away from the user, such as the cameraon the back of a smart phone. The camera is moved in a concave motion608, such that views 604 a, 604 b, and 604 c capture various parts ofcapture area 609.

With reference to FIG. 6B, shown is an example of a back-facing, convexsurround view being captured. According to various embodiments, alocally convex surround view is one in which viewing angles convergetoward a single object of interest. In some examples, a locally convexsurround view can provide the experience of orbiting about a point, suchthat a viewer can see multiple sides of the same object. This object,which may be an “object of interest,” can be segmented from the surroundview to become the content, and any surrounding data can be segmented tobecome the context. Previous technologies fail to recognize this type ofviewing angle in the media-sharing landscape.

In the present example embodiment, a user 602 is using a back-facingcamera 614 to capture images towards world 600, and away from user 602.The camera is moved in a convex motion 610, such that views 612 a, 612b, and 612 c capture various parts of capture area 611. As describedabove, world 600 can include an object of interest in some examples, andthe convex motion 610 can orbit around this object. Views 612 a, 612 b,and 612 c can include views of different sides of this object in theseexamples.

With reference to FIG. 6C, shown is an example of a front-facing,concave surround view being captured. As described in various examples,a front-facing camera refers to a device with a camera that facestowards the user, such as the camera on the front of a smart phone. Forinstance, front-facing cameras are commonly used to take “selfies”(i.e., self-portraits of the user).

In the present example embodiment, camera 620 is facing user 602. Thecamera follows a concave motion 606 such that the views 618 a, 618 b,and 618 c diverge from each other in an angular sense. The capture area617 follows a concave shape that includes the user at a perimeter.

With reference to FIG. 6D, shown is an example of a front-facing, convexsurround view being captured. In the present example embodiment, camera626 is facing user 602. The camera follows a convex motion 622 such thatthe views 624 a, 624 b, and 624 c converge towards the user 602. Thecapture area 617 follows a concave shape that surrounds the user 602.

With reference to FIG. 6E, shown is an example of a back-facing, flatview being captured. In particular example embodiments, a locally flatsurround view is one in which the rotation of the camera is smallcompared to its translation. In a locally flat surround view, theviewing angles remain roughly parallel, and the parallax effectdominates. In this type of surround view, there can also be an “objectof interest”, but its position does not remain fixed in the differentviews. Previous technologies also fail to recognize this type of viewingangle in the media-sharing landscape.

In the present example embodiment, camera 632 is facing away from user602, and towards world 600. The camera follows a generally linear motion628 such that the capture area 629 generally follows a line. The views630 a, 630 b, and 630 c have generally parallel lines of sight. Anobject viewed in multiple views can appear to have different or shiftedbackground scenery in each view. In addition, a slightly different sideof the object may be visible in different views. Using the parallaxeffect, information about the position and characteristics of the objectcan be generated in a surround view that provides more information thanany one static image.

As described above, various modes can be used to capture images for asurround view. These modes, including locally concave, locally convex,and locally linear motions, can be used during capture of separateimages or during continuous recording of a scene. Such recording cancapture a series of images during a single session.

According to various embodiments of the present disclosure, a surroundview can be generated from data acquired in numerous ways. FIG. 7Aillustrates one example of process for recording data that can be usedto generate a surround view. In this example, data is acquired by movinga camera through space. In particular, a user taps a record button 702on a capture device 700 to begin recording. As movement of the capturedevice 716 follows a generally leftward direction, an object 714 movesin a generally rightward motion across the screen, as indicated bymovement of object 716. Specifically, the user presses the record button702 in view 708, and then moves the capture device leftward in view 710.As the capture device moves leftward, object 714 appears to moverightward between views 710 and 712. In some examples, when the user isfinished recording, the record button 702 can be tapped again. In otherexamples, the user can tap and hold the record button during recording,and release to stop recording. In the present embodiment, the recordingcaptures a series of images that can be used to generate a surroundview.

According to various embodiments, different types of panoramas can becaptured in surround views, depending on the type of movement used inthe capture process. In particular, dynamic panoramas, object panoramas,and selfie panoramas can be generated based on captured data. In someembodiments, the captured data can be recorded as described with regardto FIG. 7A.

FIGS. 7B-7F illustrate examples relating to dynamic panoramas that canbe created with surround views. With particular reference to FIG. 7B,shown is one example of a dynamic panorama capture process 720. In thepresent example, a user 722 moves capture device 724 along capturemotion 726. This capture motion 726 can include rotating, waving,translating, etc. the capture device 724. During this capture process, apanorama of scene 728 is generated and dynamic content within the sceneis kept. For instance, moving objects are preserved within the panoramaas dynamic content.

With reference to FIG. 7C, shown is a specific example of a dynamicpanorama capture process 730 where a capture device 732 is rotatedthrough an axis of rotation 734. In particular, capture device 732 isrotated about its center along an axis of rotation 734. This purerotation captures a panorama of scene 736. According to variousexamples, this type of panorama can provide a “flat” scene that capturesentities in the scene at a particular point in time. This “flat” scenecan be a two-dimensional image, or can be an image projected on acylinder, surface, etc.

With reference to FIG. 7D, shown is one example of a dynamic panorama740 with dynamic content 744. Once a panorama is captured, as describedabove with regard to FIGS. 7B-7C, a dynamic panorama 740 can benavigated by a user. In the present example, dynamic content 744 isanimated when the user navigates through the dynamic panorama 740. Forinstance, as the user swipes across scene 742, the dynamic content 744can be seen moving with respect to the scene 742.

With reference to FIG. 7E, shown is one example of capturing a dynamicpanorama with a 3D effect. In the present example, if a capture deviceis not rotated exactly around its camera center (as in FIG. 7C), a 3Deffect can be obtained by moving different parts of the panorama atdifferent speeds while the user navigates through the dynamic content.Although a nearby person or object 750 would create artifacts in astandard panorama capture process if the capture device is not rotatedaround its camera center (as in FIG. 7C), these “imperfections” can beused to create a 3D impression to the user by moving the object 750 at adifferent speed when swiping/navigating through a dynamic panorama. Inparticular, the capture device 745 shown uses a capture motion 748 thatcaptures a distant scene 746 and a nearby person/object 750. Themovements of the nearby person/object 750 can be captured as 3D motionwithin the surround view, while the distant scenery 746 appears to bestatic as the user navigates through the surround view, according tovarious embodiments.

With reference to FIG. 7F, shown is one example of a dynamic panorama750 with parallax effect. Three-dimensional effects can be presented byapplying a parallax effect when swiping perpendicular to the panoramadirection 752. In particular, when swiping perpendicular to the panoramadirection, along the parallax direction 754, nearby objects aredisplaced along the parallax direction 754 while the scene at distancestays still or moves less than the nearby objects.

FIGS. 7G-7J illustrate examples relating to object panoramas that can becreated with surround views. With reference to FIG. 7G, shown is oneexample of an object panorama capture process. In particular, a capturedevice 766 is moved around an object 762 along a capture motion 760. Oneparticular example of a capture device 766 is a smartphone. The capturedevice 766 also captures a panoramic view of the background 764 asvarious views and angles of the object 762 are captured. The resultingsurround view includes a panoramic view of object 762.

In some embodiments, a surround view can be created by projecting anobject panorama onto a background panorama, an example of which is shownin FIG. 7H. In particular, a panorama 768 of this kind is built usingbackground panorama 770 and projecting a foreground object panorama 772onto the background panorama 770. In some examples, an object panoramacan be segmented content taken from a surround view, as described inmore detail with regard to FIGS. 17A-17B.

According to various embodiments, multiple objects can make up an objectpanorama. With reference to FIG. 7I, shown is one example of a captureprocess for a group of objects 780 making up an object panorama. Asshown, a capture device 776 can move around a foreground object, whichcan be a single object or a group of objects 780 located at a similardistance to the capture device. The capture device 776 can move aroundthe object or group of objects 780 along a capture motion 778, such thatvarious views and angles of the objects are captured. The resultingsurround view can include an object panorama of the group of objects 780with distant background 782 as the context.

Object panoramas allow users to navigate around the object, according tovarious examples. With reference to FIG. 7J, shown is one example ofchanging the viewing angle of an object panorama based on usernavigation. In this example, three views are shown of a surround viewpanorama 784. In the surround view panorama, a foreground object 786 isshown in front of a background panorama 788. As a user navigates thepanorama by swiping or otherwise interacting with the surround view, thelocation of the object, the viewing angle of the object, or both can bechanged. In the present example, the user can swipe in the direction ofthe main panorama axis. This navigation can rotate the foreground object786 in this view. In some examples, the distant background panorama 788may not change as the foreground object panorama rotates or otherwisemoves.

According to various embodiments, object panoramas can also includeparallax effects. These parallax effects can be seen whenswiping/navigating perpendicular to the direction of the main panoramaaxis. Similar to FIG. 7F, three-dimensional effects can be presentedwhen swiping perpendicular to the panorama direction. In particular,when swiping perpendicular to the panorama direction, along the parallaxdirection, nearby objects are displaced along the parallax directionwhile the scene at distance stays still or moves less than the nearbyobjects.

Although the previous examples relate to static content and backgroundcontext in object panoramas, dynamic content can be integrated in theobject panorama for either or both the foreground object and thebackground context. For instance, dynamic content can be featured in amanner similar to that described in conjunction with FIG. 7D. Similarly,dynamic context can also be included in object panoramas.

Another type of panorama that can be included in surround views is aselfie panorama. In some examples, a selfie panorama can be segmentedcontent taken from a surround view, as described in more detail withregard to FIGS. 17A-17B. FIGS. 7K-7L illustrate examples relating toselfie panoramas that can be created with surround views. With referenceto FIG. 7K, shown is one example of a selfie panorama capture process790. In particular, a user 794 moves a capture device 792 along capturemotion 796 while capturing images of the user 794. In some examples, thecapture device 792 can use a front-facing camera, such as one includedon a smart phone. In other examples, a digital camera or other imagerecording device can be used. A selfie panorama is created with theseimages, with background 798 providing the context.

With reference to FIG. 7L, shown is one example of a background panoramawith a selfie panorama projected on it. In the present example, asurround view panorama 723 is built from a background panorama 725 witha selfie panorama 721 projected on it. According to various examples,the selfie panorama can include a single person or multiple people,similar to the object or group of objects described in conjunction withFIG. 7I. In the present example, selfie panoramas can include dynamiccontent. For instance, the user can look at the capture device as thecapture device moves or the user can keep still while moving the capturedevice. The user's movements can be captured while the selfie panorama721 is recorded. These dynamic elements will be mapped into the panoramaand can be displayed while interacting with the resulting selfiepanorama 721. For instance, the user's blinks can be recorded andcaptured. Navigation of the selfie panorama can be done in a mannersimilar to that described in conjunction with FIG. 7J. In particular,the location and viewpoint of the person(s) in the selfie panorama 721can be changed by the user by swiping/navigating in the direction of themain panorama axis. According to various embodiments, selfie panoramas721 can also include parallax effects. These parallax effects can beseen when swiping/navigating perpendicular to the direction of the mainpanorama axis. In addition, similar to FIG. 7F, three-dimensionaleffects can be presented when swiping perpendicular to the panoramadirection. In particular, when swiping perpendicular to the panoramadirection, along the parallax direction, nearby objects are displacedalong the parallax direction while the scene at distance stays still ormoves less than the nearby objects.

As described above, various types of panoramas can be created withsurround views. In addition, surround views can be viewed and navigatedin different ways. With reference to FIG. 7M, shown is one example ofextended views of panoramas that are provided based on user navigation.In the present example, possible views 727 include a full panorama view729, recording views 731, and extended view 733. A full panorama view729 includes a full view of the information in a surround view. Therecording views 731 include the visual data captured in images and/orrecordings. The extended view 733 shows more than what is visible duringone point in time in recording views 731 but less than the full panoramaview 729. The portion of the panorama 729 that is visible in an extendedview 733 is defined by user navigation. An extended view 733 isespecially interesting for a selfie or object panorama, because theextended view follows the object/person in the panorama and shows alarger view than what was visible for the camera while recording.Essentially, more context is provided to the user in an extended view733 during navigation of the surround view.

According to various embodiments, once a series of images is captured,these images can be used to generate a surround view. With reference toFIG. 8 , shown is an example of a surround view in whichthree-dimensional content is blended with a two-dimensional panoramiccontext. In the present example embodiment, the movement of capturedevice 820 follows a locally convex motion, such that the capture devicemoves around the object of interest (i.e., a person sitting in a chair).The object of interest is delineated as the content 808, and thesurrounding scenery (i.e., the room) is delineated as the context 810.In the present embodiment, as the movement of the capture device 820moves leftwards around the content 808, the direction of contentrotation relative to the capture device 812 is in a rightward,counterclockwise direction. Views 802, 804, and 806 show a progressionof the rotation of the person sitting in a chair relative to the room.

According to various embodiments, a series of images used to generate asurround view can be captured by a user recording a scene, object ofinterest, etc. Additionally, in some examples, multiple users cancontribute to acquiring a series of images used to generate a surroundview. With reference to FIG. 9 , shown is one example of a space-timesurround view being simultaneously recorded by independent observers.

In the present example embodiment, cameras 904, 906, 908, 910, 912, and914 are positioned at different locations. In some examples, thesecameras 904, 906, 908, 910, 912, and 914 can be associated withindependent observers. For instance, the independent observers could beaudience members at a concert, show, event, etc. In other examples,cameras 904, 906, 908, 910, 912, and 914 could be placed on tripods,stands, etc. In the present embodiment, the cameras 904, 906, 908, 910,912, and 914 are used to capture views 904 a, 906 a, 908 a, 910 a, 912a, and 914 a, respectively, of an object of interest 900, with world 902providing the background scenery. The images captured by cameras 904,906, 908, 910, 912, and 914 can be aggregated and used together in asingle surround view in some examples. Each of the cameras 904, 906,908, 910, 912, and 914 provides a different vantage point relative tothe object of interest 900, so aggregating the images from thesedifferent locations provides information about different viewing anglesof the object of interest 900. In addition, cameras 904, 906, 908, 910,912, and 914 can provide a series of images from their respectivelocations over a span of time, such that the surround view generatedfrom these series of images can include temporal information and canalso indicate movement over time.

As described above with regard to various embodiments, surround viewscan be associated with a variety of capture modes. In addition, asurround view can include different capture modes or different capturemotions in the same surround view. Accordingly, surround views can beseparated into smaller parts in some examples. With reference to FIG. 10, shown is one example of separation of a complex surround-view intosmaller, linear parts. In the present example, complex surround view1000 includes a capture area 1026 that follows a sweeping L motion,which includes two separate linear motions 1022 and 1024 of camera 1010.The surround views associated with these separate linear motions can bebroken down into linear surround view 1002 and linear surround view1004. It should be noted that although linear motions 1022 and 1024 canbe captured sequentially and continuously in some embodiments, theselinear motions 1022 and 1024 can also be captured in separate sessionsin other embodiments.

In the present example embodiment, linear surround view 1002 and linearsurround view 1004 can be processed independently, and joined with atransition 1006 to provide a continuous experience for the user.Breaking down motion into smaller linear components in this manner canprovide various advantages. For instance, breaking down these smallerlinear components into discrete, loadable parts can aid in compressionof the data for bandwidth purposes. Similarly, non-linear surround viewscan also be separated into discrete components. In some examples,surround views can be broken down based on local capture motion. Forexample, a complex motion may be broken down into a locally convexportion and a linear portion. In another example, a complex motion canbe broken down into separate locally convex portions. It should berecognized that any number of motions can be included in a complexsurround view 1000, and that a complex surround view 1000 can be brokendown into any number of separate portions, depending on the application.

Although in some applications, it is desirable to separate complexsurround views, in other applications it is desirable to combinemultiple surround views. With reference to FIG. 11 , shown is oneexample of a graph that includes multiple surround views combined into amulti-surround view 1100. In this example, the rectangles representvarious surround views 1102, 1104, 1106, 1108, 1110, 1112, 1114, and1116, and the length of each rectangle indicates the dominant motion ofeach surround view. Lines between the surround views indicate possibletransitions 1118, 1120, 1122, 1124, 1126, 1128, 1130, and 1132 betweenthem.

In some examples, a surround view can provide a way to partition a sceneboth spatially and temporally in a very efficient manner. For very largescale scenes, multi-surround view 1100 data can be used. In particular,a multi-surround view 1100 can include a collection of surround viewsthat are connected together in a spatial graph. The individual surroundviews can be collected by a single source, such as a single user, or bymultiple sources, such as multiple users. In addition, the individualsurround views can be captured in sequence, in parallel, or totallyuncorrelated at different times. However, in order to connect theindividual surround views, there must be some overlap of content,context, or location, or of a combination of these features.Accordingly, any two surround views would need to have some overlap incontent, context, and/or location to provide a portion of amulti-surround view 1100. Individual surround views can be linked to oneanother through this overlap and stitched together to form amulti-surround view 1100. According to various examples, any combinationof capture devices with either front, back, or front and back camerascan be used.

In some embodiments, multi-surround views 1100 can be generalized tomore fully capture entire environments. Much like “photo tours” collectphotographs into a graph of discrete, spatially-neighboring components,multiple surround views can be combined into an entire scene graph. Insome examples, this can be achieved using information obtained from butnot limited to: image matching/tracking, depth matching/tracking, IMU,user input, and/or GPS. Within such a graph or multi-surround view, auser can switch between different surround views either at the endpoints of the recorded motion or wherever there is an overlap with othersurround views in the graph. One advantage of multi-surround views over“photo tours” is that a user can navigate the surround views as desiredand much more visual information can be stored in surround views. Incontrast, traditional “photo tours” typically have limited views thatcan be shown to the viewer either automatically or by allowing the userto pan through a panorama with a computer mouse or keystrokes.

According to various embodiments, a surround view is generated from aset of images. These images can be captured by a user intending toproduce a surround view or retrieved from storage, depending on theapplication. Because a surround view is not limited or restricted withrespect to a certain amount of visibility, it can provide significantlymore visual information about different views of an object or scene.More specifically, although a single viewpoint may be ambiguous toadequately describe a three-dimensional object, multiple views of theobject can provide more specific and detailed information. Thesemultiple views can provide enough information to allow a visual searchquery to yield more accurate search results. Because a surround viewprovides views from many sides of an object, distinctive views that areappropriate for search can be selected from the surround view orrequested from a user if a distinctive view is not available. Forinstance, if the data captured or otherwise provided is not sufficientto allow recognition or generation of the object or scene of interestwith a sufficiently high certainty, a capturing system can guide a userto continue moving the capturing device or provide additional imagedata. In particular embodiments, if a surround view is determined toneed additional views to produce a more accurate model, a user may beprompted to provide additional images.

With reference to FIG. 12 , shown is one example of a process forprompting a user for additional images 1200 to provide a more accuratesurround view. In the present example, images are received from acapturing device or storage at 1202. Next, a determination is madewhether the images provided are sufficient to allow recognition of anobject of interest at 1204. If the images are not sufficient to allowrecognition of an object of interest, then a prompt is given for theuser to provide additional image(s) from different viewing angles at1206. In some examples, prompting a user to provide one or moreadditional images from different viewing angles can include suggestingone or more particular viewing angles. If the user is actively capturingimages, the user can be prompted when a distinct viewing angle isdetected in some instances. According to various embodiments,suggestions to provide one or more particular viewing angles can bedetermined based on the locations associated with the images alreadyreceived. In addition, prompting a user to provide one or moreadditional images from different viewing angles can include suggestingusing a particular capture mode such as a locally concave surround view,a locally convex surround view, or a locally flat surround view,depending on the application.

Next, the system receives these additional image(s) from the user at1208. Once the additional images are received, a determination is madeagain whether the images are sufficient to allow recognition of anobject of interest. This process continues until a determination is madethat the images are sufficient to allow recognition of an object ofinterest. In some embodiments, the process can end at this point and asurround view can be generated.

Optionally, once a determination is made that the images are sufficientto allow recognition of an object of interest, then a determination canthen be made whether the images are sufficient to distinguish the objectof interest from similar but non-matching items at 1210. Thisdetermination can be helpful especially when using visual search,examples of which are described in more detail below with regards toFIGS. 19-22 . In particular, an object of interest may havedistinguishing features that can be seen from particular angles thatrequire additional views. For instance, a portrait of a person may notsufficiently show the person's hairstyle if only pictures are taken fromthe front angles. Additional pictures of the back of the person may needto be provided to determine whether the person has short hair or just apulled-back hairstyle. In another example, a picture of a person wearinga shirt might warrant additional prompting if it is plain on one sideand additional views would show prints or other insignia on the sleevesor back, etc.

In some examples, determining that the images are not sufficient todistinguish the object of interest from similar but non-matching itemsincludes determining that the number of matching search results exceedsa predetermined threshold. In particular, if a large number of searchresults are found, then it can be determined that additional views maybe needed to narrow the search criteria. For instance, if a search of amug yields a large number of matches, such as more than 20, thenadditional views of the mug may be needed to prune the search results.

If the images are not sufficient to distinguish the object of interestfrom similar but non-matching items at 1210, then a prompt is given forthe user to provide additional image(s) from different viewing angles at1212. In some examples, prompting a user to provide one or moreadditional images from different viewing angles can include suggestingone or more particular viewing angles. If the user is actively capturingimages, the user can be prompted when a distinct viewing angle isdetected in some instances. According to various embodiments,suggestions to provide one or more particular viewing angles can bedetermined based on the locations associated with the images alreadyreceived. In addition, prompting a user to provide one or moreadditional images from different viewing angles can include suggestingusing a particular capture mode such as a locally concave surround view,a locally convex surround view, or a locally flat surround view,depending on the application.

Next, the system receives these additional image(s) from the user at1214. Once the additional images are received, a determination is madeagain whether the images are sufficient to distinguish the object ofinterest from similar but non-matching items. This process continuesuntil a determination is made that the images are sufficient todistinguish the object of interest from similar but non-matching items.Next, the process ends and a surround view can be generated from theimages.

With reference to FIGS. 13A-13B, shown are examples of promptsrequesting additional images from a user in order to produce a moreaccurate surround view. In particular, a device 1300 is shown with asearch screen. In FIG. 13A, an example of a visual search query 1302 isprovided. This visual search query 1302 includes an image of a whitemug. The results 1306 include various mugs with a white background. Inparticular embodiments, if a large amount of search results is found, aprompt 1304 can be provided to request additional image data from theuser for the search query.

In FIG. 13B, an example of another visual search query 1310 is providedin response to prompt 1304 in FIG. 13A. This visual search query 1310provides a different viewpoint of the object and provides more specificinformation about the graphics on the mug. This visual search query 1310yields new results 1312 that are more targeted and accurate. In someexamples, an additional prompt 1308 can be provided to notify the userthat the search is complete.

Once a surround view is generated, it can be used in variousapplications, in particular embodiments. One application for a surroundview includes allowing a user to navigate a surround view or otherwiseinteract with it. According to various embodiments, a surround view isdesigned to simulate the feeling of being physically present in a sceneas the user interacts with the surround view. This experience dependsnot only on the viewing angle of the camera, but on the type of surroundview that is being viewed. Although a surround view does not need tohave a specific fixed geometry overall, different types of geometriescan be represented over a local segment of a surround view such as aconcave, convex, and flat surround view, in particular embodiments.

In particular example embodiments, the mode of navigation is informed bythe type of geometry represented in a surround view. For instance, withconcave surround views, the act of rotating a device (such as asmartphone, etc.) can mimic that of rotating a stationary observer whois looking out at a surrounding scene. In some applications, swiping thescreen in one direction can cause the view to rotate in the oppositedirection. This effect is akin to having a user stand inside a hollowcylinder and pushing its walls to rotate around the user. In otherexamples with convex surround views, rotating the device can cause theview to orbit in the direction it is leaning into, such that the objectof interest remains centered. In some applications, swiping the screenin one direction causes the viewing angle to rotate in the samedirection: this creates the sensation of rotating the object of interestabout its axis or having the user rotate around the object. In someexamples with flat views, rotating or moving a device can cause the viewto translate in the direction of the device's movement. In addition,swiping the screen in one direction can cause the view to translate inthe opposite direction, as if pushing foreground objects to the side.

In some examples, a user may be able to navigate a multi-surround viewor a graph of surround views in which individual surround views can beloaded piece by piece and further surround views may be loaded whennecessary (e.g. when they are adjacent to/overlap the current surroundview and/or the user navigates towards them). If the user reaches apoint in a surround view where two or more surround views overlap, theuser can select which of those overlapping surround views to follow. Insome instances, the selection of which surround view to follow can bebased on the direction the user swipes or moves the device.

With reference to FIG. 14 , shown is one example of a process fornavigating a surround view 1400. In the present example, a request isreceived from a user to view an object of interest in a surround view at1402. According to various embodiments, the request can also be ageneric request to view a surround view without a particular object ofinterest, such as when viewing a landscape or panoramic view. Next, athree-dimensional model of the object is accessed at 1404. Thisthree-dimensional model can include all or a portion of a storedsurround view. For instance, the three-dimensional model can be asegmented content view in some applications. An initial image is thensent from a first viewpoint to an output device at 1406. This firstviewpoint serves as a starting point for viewing the surround view onthe output device.

In the present embodiment, a user action is then received to view theobject of interest from a second viewpoint. This user action can includemoving (e.g. tilting, translating, rotating, etc.) an input device,swiping the screen, etc., depending on the application. For instance,the user action can correspond to motion associated with a locallyconcave surround view, a locally convex surround view, or a locally flatsurround view, etc. According to various embodiments, an object view canbe rotated about an axis by rotating a device about the same axis. Forexample, the object view can be rotated along a vertical axis byrotating the device about the vertical axis. Based on thecharacteristics of the user action, the three-dimensional model isprocessed at 1410. For instance, movement of the input device can bedetected and a corresponding viewpoint of the object of interest can befound. Depending on the application, the input device and output devicecan both be included in a mobile device, etc. In some examples, therequested image corresponds to an image captured prior to generation ofthe surround view. In other examples the requested image is generatedbased on the three-dimensional model (e.g. by interpolation, etc.). Animage from this viewpoint can be sent to the output device at 1412. Insome embodiments, the selected image can be provided to the outputdevice along with a degree of certainty as to the accuracy of theselected image. For instance, when interpolation algorithms are used togenerate an image from a particular viewpoint, the degree of certaintycan vary and may be provided to a user in some applications. In otherexamples, a message can be provided to the output device indicating ifthere is insufficient information in the surround view to provide therequested images.

In some embodiments, intermediate images can be sent between the initialimage at 1406 and the requested image at 1412. In particular, theseintermediate images can correspond to viewpoints located between a firstviewpoint associated with the initial image and a second viewpointassociated with the requested image. Furthermore, these intermediateimages can be selected based on the characteristics of the user action.For instance, the intermediate images can follow the path of movement ofthe input device associated with the user action, such that theintermediate images provide a visual navigation of the object ofinterest.

With reference to FIG. 15 , shown is an example of swipe-basednavigation of a surround view. In the present example, three views ofdevice 1500 are shown as a user navigates a surround view. Inparticular, the input 1510 is a swipe by the user on the screen ofdevice 1500. As the user swipes from right to left, the object ofinterest moves relative to the direction of swipe 1508. Specifically, asshown by the progression of images 1506, 1504, and 1502, the input 1510allows the user to rotate around the object of interest (i.e., the manwearing sunglasses).

In the present example, a swipe on a device screen can correspond torotation of a virtual view. However, other input modes can be used inother example embodiments. For instance, a surround view can also benavigated by tilting a device in various directions and using the deviceorientation direction to guide the navigation in the surround view. Inanother example, the navigation can also be based on movement of thescreen by the user. Accordingly, a sweeping motion can allow the user tosee around the surround view as if the viewer were pointing the deviceat the object of interest. In yet another example, a website can be usedto provide interaction with the surround view in a web-browser. In thisexample, swipe and/or motion sensors may be unavailable, and can bereplaced by interaction with a mouse or other cursor or input device.

According to various embodiments, surround views can also includetagging that can be viewed during navigation. Tagging can provideidentification for objects, people, products, or other items within asurround view. In particular, tagging in a surround view is a verypowerful tool for presenting products to users/customers and promotingthose elements or items. In one example, a tag 1512 can follow thelocation of the item that is tagged, such that the item can be viewedfrom different angles while the tag locations still stay valid. The tags1512 can store different types of data, such as a name (e.g. user name,product name, etc.), a description, a link to a website/webshop, priceinformation, a direct option for purchasing a tagged object, a list ofsimilar objects, etc. In some examples, the tags can become visible whena user selects an item in a surround view. In other examples, the tagscan be automatically displayed. In addition, additional information canbe accessed by selecting a tag 1512 in some applications. For instance,when a user selects a tag, additional information can be displayed onscreen such as a description, link, etc.

In some embodiments, a user can create a tag 1512 by selecting either apoint or a region in one viewpoint of a surround view. This point orregion is then automatically propagated into other viewpoints.Alternatively, tag locations can be automatically suggested to the userby an application based on different information, such as facedetection, object detection, objects in focus, objects that areidentified as foreground, etc. In some examples, object detection can bemade from a database of known objects or object types/classes.

In the present example, tag 1512 identifies a shirt in the surroundview. Of course, any text or title can be included, such as a name,brand, etc. This tag 1512 can be mapped to a particular location in thesurround view such that the tag is associated with the same location orpoint in any view selected. As described above, tag 1512 can includeadditional information that can be accessed by tapping or otherwiseselecting the tag, in some embodiments. Although tagging is shown inFIG. 15 , it should be noted that surround views may not include taggingin some examples.

According to various embodiments, surround views can be stored andaccessed in various ways. In addition, surround views can be used inmany applications. With reference to FIG. 16A, shown are examples of asharing service for surround views on a mobile device 1602 and browser1604. The mobile device 1602 and browser 1604 are shown as alternatethumbnail displays 1600, because the surround views can be accessed byeither interface, depending on the application. According to variousembodiments, a set of surround views can be presented to a user indifferent ways, including but not limited to: a gallery, a feed, and/ora website. For instance, a gallery can be used to present a collectionof thumbnails to a user. These thumbnails can be selected from thesurround views either by the user or automatically. In some examples,the size of the thumbnails can vary based on characteristics such as,but not limited to: an automatically selected size that is based on thestructure and size of the content it contains; and/or the popularity ofthe surround view. In another example, a feed can be used to presentsurround views using interactive thumbnails.

In the present example, surround view thumbnails from a mobile device1602 include thumbnails 1604 and title/label/description 1604. Thethumbnails 1604 can include an image from the surround view. Thetitle/label/description 1604 can include information about the surroundview such as title, file name, description of the content, labels, tags,etc.

Furthermore, in the present example, surround view thumbnails from abrowser 1604 include thumbnails 1606, title/label/description 1608, andnotifications 1610. The thumbnails 1606 can include an image from thesurround view. The title/label/description 1608 can include informationabout the surround view such as title, file name, description of thecontent, labels, tags, etc. In addition, notifications 1610 can includeinformation such as comments on a surround view, updates about matchingcontent, suggested content, etc. Although not shown on the mobileversion, notifications can also be included, but may be omitted in theinterest of layout and space considerations in some embodiments. In someexamples, notifications can be provided as part of a surround viewapplication on a mobile device.

With reference to FIG. 16B, shown are examples of surround view-relatednotifications on a mobile device. In particular, alternativenotification screens 1620 for a device 1622 are shown that includedifferent formats for notifications. In some examples, a user cannavigate between these screens depending on the user's preferences.

In the present example, screen 1624 includes a notification 1626 thatincludes a recommendation to the user based on content from recentsurround views. In particular, the recommendation relates to a trip toGreece based on the application's finding that the user has an affinityfor statues. This finding can be inferred from content found in theuser's stored or recently browsed surround views, in some examples.

In the present example, screen 1628 includes notifications 1630 based oncontent from surround views that the user has stored, browsed, etc. Forinstance, one notification is a recommendation for a pair of shoesavailable at a nearby retailer that are similar to the user's shoes asprovided in a surround view model. The recommendation also includes alink to a map to the retailer. This recommendation can be based on asurround view that the user has saved of a pair of shoes. The othernotification is a recommendation to connect to another user that sharesa common interest/hobby. In this example, the recommendation is based onthe user's detected interest in hats. These recommendations can beprovided automatically in some applications as “push” notifications. Thecontent of the recommendations can be based on the user's surround viewsor browsing history, and visual search algorithms, such as thosedescribed with regard to FIGS. 19-22 , can be used in some examples.

Screen 1630 shows another form of notification 1632 in the presentexample. Various icons for different applications are featured on screen1630. The icon for the surround view application includes a notification1632 embedded into the icon that shows how many notifications arewaiting for the user. When the user selects the icon, the notificationscan be displayed and/or the application can be launched, according tovarious embodiments.

According to various embodiments of the present disclosure, surroundviews can be used to segment, or separate, objects from static ordynamic scenes. Because surround views include distinctive 3D modelingcharacteristics and information derived from image data, surround viewsprovide a unique opportunity for segmentation. In some examples, bytreating an object of interest as the surround view content, andexpressing the remaining of the scene as the context, the object can besegmented out and treated as a separate entity. Additionally, thesurround view context can be used to refine the segmentation process insome instances. In various embodiments, the content can be chosen eitherautomatically or semi-automatically using user guided interaction. Oneimportant use for surround view object segmentation is in the context ofproduct showcases in e-commerce, an example of which is shown in FIG.17B. In addition, surround view-based object segmentation can be used togenerate object models that are suited for training artificialintelligence search algorithms that can operate on large databases, inthe context of visual search applications.

With reference to FIG. 17 , shown is one example of a process forproviding object segmentation 1700. At 1702, a first surround view of anobject is obtained. Next, content is selected from the first surroundview at 1704. In some examples, the content is selected automaticallywithout user input. In other examples, the content is selectedsemi-automatically using user-guided interaction. The content is thensegmented from the first surround view at 1706. In some examples, thecontent is segmented by reconstructing a model of the content inthree-dimensions based on the information provided in the first surroundview, including images from multiple camera viewpoints. In particularexample embodiments, a mechanism for selecting and initializing asegmentation algorithm based on iterative optimization algorithms (suchas graphical models) can be efficiently employed by reconstructing theobject of interest, or parts of it, in three-dimensions from multiplecamera viewpoints available in a surround view. This process can berepeated over multiple frames, and optimized until segmentation reachesa desired quality output. In addition, segmenting the content caninclude using the context to determine parameters of the content.

In the present example, once the content is segmented from the firstsurround view, a second surround view is generated that includes theobject without the content or scenery surrounding the object. At 1708,this second surround view is provided. In some examples, the secondsurround view can then be stored in a database. This second surroundview can be used in various applications. For instance, the segmentedcontent includes a product for use in e-commerce. As illustrated in FIG.17B, the segmented content can be used to show a product from variousviewpoints. Another application includes using the second surround viewas an object model for artificial intelligence training. In yet anotherapplication, the second surround view can be used in 3D printing. Inthis application, data from the second surround view is to a 3D printer.

Although the present example describes segmenting out content from afirst surround view, it should be noted that context can also besegmented out in other examples. For instance, the background scenerycan be segmented out and presented as a second surround view in someapplications. In particular, the context can be selected from the firstsurround view and the context can be segmented from the first surroundview, such that the context is separated into a distinct interactivemodel. The resulting surround view would then include the scenerysurrounding an object but exclude the object itself. A segmented contextmodel can also be used in various applications. For instance, data fromthe resulting surround view can be sent to a 3D printer. In someexamples, this could be printed as a panoramic background on a flat orcurved surface. If a context model is also printed, then the object ofinterest can be placed in front of the panoramic background to produce athree-dimensional “photograph” or model of the surround view. In anotherapplication, the segmented out context can be used as background to adifferent object of interest. Alternatively, a segmented out content canbe placed in a new segmented out context. In these examples, providingan alternative content or context allows objects of interest to beplaced into new backgrounds, etc. For instance, a surround view of aperson could be placed in various background contexts, showing theperson standing on a beach in one surround view, and standing in thesnow in another surround view.

With reference to FIG. 17B, shown is one example of a segmented objectviewed from different angles. In particular, a rotational view 1720 isshown of an athletic shoe. Object views 1722, 1724, 1726, 1728, and 1730show the athletic shoe from various angles or viewpoints. As shown, theobject itself is shown without any background or context. According tovarious embodiments, these different views of the segmented object canbe automatically obtained from surround view content. One application ofthese types of rotational views is in e-commerce to show product viewsfrom different angles. Another application can be in visual search,according to various embodiments.

According to various embodiments, surround views can be generated fromdata obtained from various sources and can be used in numerousapplications. With reference to FIG. 18 , shown is a block diagramillustrating one example of various sources that can be used forsurround view generation and various applications that can be used witha surround view. In the present example, surround view generation andapplications 1800 includes sources for image data 1808 such as internetgalleries 1802, repositories 1804, and users 1806. In particular, therepositories can include databases, hard drives, storage devices, etc.In addition, users 1806 can include images and information obtaineddirectly from users such as during image capture on a smartphone, etc.Although these particular examples of data sources are indicated, datacan be obtained from other sources as well. This information can begathered as image data 1808 to generate a surround view 1810, inparticular embodiments.

In the present example, a surround view 1810 can be used in variousapplications. As shown, a surround view can be used in applications suchas e-commerce 1812, visual search 1814, 3D printing 1816, file sharing1818, user interaction 1820, and entertainment 1822. Of course, thislist is only illustrative, and surround views can also be used in otherapplications not explicitly noted.

As described above with regard to segmentation, surround views can beused in e-commerce 1812. For instance, surround views can be used toallow shoppers to view a product from various angles. In someapplications, shoppers can even use surround views to determine sizing,dimensions, and fit. In particular, a shopper can provide a self-modeland determine from surround views whether the product would fit themodel. Surround views can also be used in visual search 1814 asdescribed in more detail below with regard to FIGS. 19-22 . Some of thevisual search applications can also relate to e-commerce, such as when auser is trying to find a particular product that matches a visual searchquery.

Another application of segmentation includes three-dimensional printing(3D printing) 1816. Three-dimensional printing has been recentlyidentified as one of the future disruptive technologies that willimprove the global economy in the next decade. According to variousembodiments, content can be 3D printed from a surround view. Inaddition, the panoramic background context in a surround view can alsobe printed. In some examples, a printed background context cancomplement the final 3D printed product for users that would like topreserve memories in a 3D printed format. For instance, the contextcould be printed either as a flat plane sitting behind the 3D content,or as any other geometric shape (spherical, cylindrical, U shape, etc.).

As described above with regard to FIG. 16A, surround views can be storedwith thumbnail views for user access. This type of application can beused for file sharing 1818 between users in some examples. For instance,a site can include infrastructure for users to share surround views in amanner similar to current photo sharing sites. File sharing 1818 canalso be implemented directly between users in some applications.

Also as described with regard to FIGS. 14 and 15 , user interaction isanother application of surround views. In particular, a user cannavigate through a surround view for their own pleasure orentertainment. Extending this concept to entertainment 1822, surroundviews can be used in numerous ways. For instance, surround views can beused in advertisements, videos, etc.

As previously described, one application of surround views is visualsearch. FIGS. 19, 20, and 22 depict examples of visual search usingsurround views. According to various embodiments, using surround viewscan provide much higher discriminative power in search results than anyother digital media representation to date. In particular, the abilityto separate content and context in a surround view is an importantaspect that can be used in visual search.

Existing digital media formats such as 2D images are unsuitable forindexing, in the sense that they do not have enough discriminativeinformation available natively. As a result, many billions of dollarsare spent in research on algorithms and mechanisms for extracting suchinformation from them. This has resulted in satisfactory results forsome problems, such as facial recognition, but in general the problem offiguring out a 3D shape from a single image is ill-posed in existingtechnologies. Although the level of false positives and negatives can bereduced by using sequences of images or 2D videos, the 3D spatialreconstruction methods previously available are still inadequate.

According to various embodiments, additional data sources such aslocation-based information, which are used to generate surround views,provide valuable information that improves the capability of visualrecognition and search. In particular example embodiments, twocomponents of a surround view, the context and the content, bothcontribute significantly in the visual recognition process. Inparticular example embodiments, the availability of three-dimensionalinformation that the content offers can significantly reduce the numberof hypotheses that must be evaluated to recognize a query object or partof a scene. According to various embodiments, the content'sthree-dimensional information can help with categorization (i.e.,figuring out the general category that an object belongs to), and thetwo-dimensional texture information can indicate more about a specificinstance of the object. In many cases, the context information in asurround view can also aid in the categorization of a query object, byexplaining the type of scene in which the query object is located.

In addition to providing information that can be used to find a specificinstance of an object, surround views are also natively suited foranswering questions such as: “what other objects are similar in shapeand appearance?” Similar to the top-N best matches provided in responseto a web search query, a surround view can be used with objectcategorization and recognition algorithms to indicate the “closestmatches,” in various examples.

Visual search using surround views can be used and/or implemented invarious ways. In one example, visual search using surround views can beused in object recognition for robotics. In another example, visualsearch using surround views can be used in social media curation. Inparticular, by analyzing the surround view data being posted to varioussocial networks, and recognizing objects and parts of scenes, better#hashtags indices can be automatically generated. By generating thistype of information, feeds can be curated and the search experience canbe enhanced.

Another example in which visual search using surround views can be usedis in a shopping context that can be referred to as “Search and Shop.”In particular, this visual search can allow recognition of items thatare similar in shape and appearance, but might be sold at differentprices in other stores nearby. For instance, with reference to FIG. 21 ,a visual search query may yield similar products available for purchase.

In yet another example in which visual search using surround views canbe used is in a shopping context that can be referred to as “Search andFit.” According to various embodiments, because surround view content isthree-dimensional, precise measurements can be extracted and thisinformation can be used to determine whether a particular objectrepresented in a surround view would fit in a certain context (e.g., ashoe fitting a foot, a lamp fitting a room, etc.).

In another instance, visual search using surround views can also be usedto provide better marketing recommendation engines. For example, byanalyzing the types of objects that appear in surround views generatedby various users, questions such as “what type of products do peoplereally use in their daily lives” can be answered in a natural, private,and non-intrusive way. Gathering this type of information can facilitateimproved recommendation engines, decrease and/or stop unwanted spam ormarketing ads, thereby increasing the quality of life of most users.FIG. 16B shows one implementation in which recommendations can beprovided according to various embodiments of the present disclosure.

With reference to FIG. 19 , shown is one example of a process forproviding visual search of an object 1900, where the search queryincludes a surround view of the object and the data searched includesthree-dimensional models. At 1902, a visual search query that includes afirst surround view is received. This first surround view is thencompared to stored surround views at 1904. In some embodiments, thiscomparison can include extracting first measurement information for theobject in the first surround view and comparing it to second measurementinformation extracted from the one or more stored surround views. Forinstance, this type of measurement information can be used for searchingitems such as clothing, shoes, or accessories.

Next, a determination is made whether any stored surround viewscorrespond to the first surround view at 1906. In some examples, thisdetermination is based on whether the subject matter in any of thestored surround views is similar in shape to the object in the firstsurround view. In other examples, this determination is based on whetherany of the subject matter in the stored surround views is similar inappearance to the object in the first surround view. In yet otherexamples, this determination is based on whether any subject matter inthe stored surround views include similar textures included in the firstsurround view. In some instances, this determination is based on whetherany of the contexts associated with the stored surround views match thecontext of the first surround view. In another example, thisdetermination is based on whether the measurement information associatedwith a stored surround view dimensionally fits the object associatedwith the first surround view. Of course any of these bases can be usedin conjunction with each other.

Once this determination is made, a ranked list of matching results isgenerated at 1908. In some embodiments, generating a ranked list ofmatching results includes indicating how closely any of the storedsurround views dimensionally fits the object associated with the firstmeasurement information. According to various embodiments, this rankedlist can include displaying thumbnails of matching results. In someexamples, links to retailers can be included with the thumbnails.Additionally, information about the matching results such as name,brand, price, sources, etc. can be included in some applications.

Although the previous example includes using a surround view as a visualsearch query to search through stored surround views orthree-dimensional models, current infrastructure still includes a vaststore of two-dimensional images. For instance, the internet providesaccess to numerous two-dimensional images that are easily accessible.Accordingly, using a surround view to search through storedtwo-dimensional images for matches can provide a useful application ofsurround views with the current two-dimensional infrastructure.

With reference to FIG. 20 , shown is one example of a process forproviding visual search of an object 2000, where the search queryincludes a surround view of the object and the data searched includestwo-dimensional images. At 2002, a visual search query that includes afirst surround view is received. Next, object view(s) are selected fromthe surround view at 2004. In particular, one or more two-dimensionalimages are selected from the surround view. Because these object view(s)will be compared to two-dimensional stored images, selecting multipleviews can increase the odds of finding a match. Furthermore, selectingone or more object views from the surround view can include selectingobject views that provide recognition of distinctive characteristics ofthe object.

In the present example, the object view(s) are then compared to storedimages at 2006. In some embodiments, one or more of the stored imagescan be extracted from stored surround views. These stored surround viewscan be retrieved from a database in some examples. In various examples,comparing the one or more object views to the stored images includescomparing the shape of the object in the surround view to the storedimages. In other examples, comparing the one or more object views to thestored images includes comparing the appearance of the object in thesurround view to the stored images. Furthermore, comparing the one ormore object views to the stored images can include comparing the textureof the object in the surround view to the stored images. In someembodiments, comparing the one or more object views to the stored imagesincludes comparing the context of the object in the surround view to thestored images. Of course any of these criteria for comparison can beused in conjunction with each other.

Next, a determination is made whether any stored images correspond tothe object view(s) at 2008. Once this determination is made, a rankedlist of matching results is generated at 2010. According to variousembodiments, this ranked list can include displaying thumbnails ofmatching results. In some examples, links to retailers can be includedwith the thumbnails. Additionally, information about the matchingresults such as name, brand, price, sources, etc. can be included insome applications.

With reference to FIG. 21 , shown is an example of a visual searchprocess 2100. In the present example, images are obtained at 2102. Theseimages can be captured by a user or pulled from stored files. Next,according to various embodiments, a surround view is generated based onthe images. This surround view is then used as a visual search querythat is submitted at 2104. In this example, a surround view can be usedto answer questions such as “which other objects in a database look likethe query object.” As illustrated, surround views can help shift thevisual search paradigm from finding other “images that look like thequery,” to finding other “objects that look like the query,” due totheir better semantic information capabilities. As described with regardto FIGS. 19 and 20 above, the surround view can then be compared to thestored surround views or images and a list of matching results can beprovided at 2106.

Although the previous examples of visual search include using surroundviews as search queries, it may also be useful to provide search queriesfor two-dimensional images in some embodiments. With reference to FIG.22 , shown is an example of a process for providing visual search of anobject 2200, where the search query includes a two-dimensional view ofthe object and the data searched includes surround view(s). At 2202, avisual search query that includes a two-dimensional view of an object tobe searched is received. In some examples, the two-dimensional view isobtained from an object surround view, wherein the object surround viewincludes a three-dimensional model of the object. Next, thetwo-dimensional view is compared to surround views at 2204. In someexamples, the two-dimensional view can be compared to one or morecontent views in the surround views. In particular, the two-dimensionalview can be compared to one or more two-dimensional images extractedfrom the surround views from different viewing angles. According tovarious examples, the two-dimensional images extracted from the surroundviews correspond to viewing angles that provide recognition ofdistinctive characteristics of the content. In other examples, comparingthe two-dimensional view to one or more surround views includescomparing the two-dimensional view to one or more content models.Various criteria can be used to compare the images or models such as theshape, appearance, texture, and context of the object. Of course any ofthese criteria for comparison can be used in conjunction with eachother.

With reference to FIG. 23 , shown is a particular example of a computersystem that can be used to implement particular examples of the presentdisclosure. For instance, the computer system 2300 can be used toprovide surround views according to various embodiments described above.According to particular example embodiments, a system 2300 suitable forimplementing particular embodiments of the present disclosure includes aprocessor 2301, a memory 2303, an interface 2311, and a bus 2315 (e.g.,a PCI bus). The interface 2311 may include separate input and outputinterfaces, or may be a unified interface supporting both operations.When acting under the control of appropriate software or firmware, theprocessor 2301 is responsible for such tasks such as optimization.Various specially configured devices can also be used in place of aprocessor 2301 or in addition to processor 2301. The completeimplementation can also be done in custom hardware. The interface 2311is typically configured to send and receive data packets or datasegments over a network. Particular examples of interfaces the devicesupports include Ethernet interfaces, frame relay interfaces, cableinterfaces, DSL interfaces, token ring interfaces, and the like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as packet switching, media control and management.

According to particular example embodiments, the system 2300 uses memory2303 to store data and program instructions and maintained a local sidecache. The program instructions may control the operation of anoperating system and/or one or more applications, for example. Thememory or memories may also be configured to store received metadata andbatch requested metadata.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present disclosurerelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include hard disks,floppy disks, magnetic tape, optical media such as CD-ROM disks andDVDs; magneto-optical media such as optical disks, and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory devices (ROM) and programmable read-only memorydevices (PROMs). Examples of program instructions include both machinecode, such as produced by a compiler, and files containing higher levelcode that may be executed by the computer using an interpreter.

FIGS. 24A-C illustrate example views of a virtual reality environment2400 from different angles, in accordance with various embodiments ofthe present disclosure. In FIGS. 24A-C, there are three content modelswithin a virtual reality fashion environment 2400. One content model isa necklace on a display bust 2404 located on the left of another contentmodel 2406 of a woman in a dress wearing sun glasses and holding apurse. The woman model 2406 is located directly to the right of thenecklace display bust 2404 in FIG. 24A. Last, the third content model isa pair of high heel shoes 2408. In addition to the three content models,virtual reality fashion environment 2400 also includes a context model2402 as a background corresponding to a design room that includes walls,a “fashion” billboard 2410, a floor, and ceiling. Once a user engageswith the VR system, the user is “placed in” or “enters” VR environment2400/design room 2402. FIGS. 24A-24C convey what a user sees as the usermoves around. FIG. 24A shows a potential “starting point” for the useronce the user “enters” the room 2402. FIG. 24B shows a view of virtualreality room 2402 after the user has moved around to the left of thestarting point towards the necklace bust 2404 and then turned toward theobjects in the center of the room. As demonstrated in FIG. 24B, theobjects remain in their respective positions as the user moves about theroom. In some embodiments, this is accomplished using zoom and shiftingfunctions to emulate the user moving around, while maintaining staticobject locations relative to the dimensions of the room. The rotationsto different angles are provided using the functions of the MIDMR, asprovided herein. Thus, the objects occupy a three dimensional spacewithin the virtual reality environment 2400. The user can circle aroundthe objects and view the objects from different angles. FIG. 24Cprovides an example view of the user moving toward the right of thewoman model 2406 toward the pair of high heel shoes 2408. Asdemonstrated, the shoes 2408 appear bigger because the user has walkedtoward the shoes relative to the user's original starting position. Aspreviously mentioned, in some embodiments, this is accomplished using azoom function. In some embodiments, although the objects appear to bethree dimensional objects, each view of the objects, determined viasensors (e.g. gyroscopic equipment, GPS, triangulation, laser and motiondetectors, etc.) in the VR system, is generated using an actual image ofthe object obtained using the techniques and systems described above. Insuch embodiments, as described above the object models are made directlythrough fusing different images together in a seamless or near seamlessmanner. In other embodiments, three dimensional models are actuallycreated via fusing of the images as described elsewhere in theapplication.

As demonstrated in FIGS. 24A-C, as the user moves about the room, thecontext models also present different views in conjunction with theuser's detected movements. For example, in FIG. 24B, the “fashion”billboard 2410 is closer to the user as compared to the view provided inFIG. 24A and only the portion closer to the right wall is visible inFIG. 24B. Should the user turn or pivot to the left, the billboard 2410would come more into view. As described above, the content models can begenerated separately from the context models or they can be generatedtogether from a real world scene. In some embodiments, the contextmodels are replaced with a real-time dynamic real world environment, oraugmented reality (AR). In such embodiments, the virtual reality room2402 is replaced with the real world using constant sensor feedback,e.g. a camera, GPS, etc. The locations of the objects are still staticrelative to the “background” (i.e., the real world) and the user movesabout the “room” by moving through the real world.

FIGS. 25A-G illustrate example views of a virtual reality environment2500 with content model manipulation, in accordance with variousembodiments of the present disclosure. As described above, in variousembodiments, the virtual reality system provides for techniques andmechanisms for manipulating content models within the virtual realityenvironment. In such embodiments, rather than walking around a contentmodel through the virtual reality space, the user can directly accessdifferent views of the content model by directly interacting with,manipulating, and/or rotating the content model. FIG. 25A shows contentmodel 2502 in a virtual reality setting. The content model 2502 iscapable of being manipulated by the user. For example the user canrotate the model as shown in FIG. 25B. The actions for rotating themodel can be similar to how the user would rotate an object in the realworld. In some embodiments, sensors in the virtual reality system cansense the motion and detect predetermined motions stored in memory ofthe system. Once the user performs such predetermined actions within athreshold vicinity of the content model, the system registers thedetected predetermined motion and applies the effect to the contentmodel accordingly. The virtual reality room 2500 also includes actiontabs such as “Next,” for moving to the next content model being stored(as depicted in FIGS. 25F and 25G), and “Recenter,” for recentering thecontent model after manipulating the content model away from theoriginal starting point. In FIG. 25F, content model 2502 has beenmanipulated to display the feet of the model. The user selects the“Next” button 2504 revealing content model 2518. As shown in FIG. 25G,the user has also selected the “Recenter” button to reset the view fromthe content model's feet (as show in FIG. 25F) to the original full bodyview.

As shown in FIG. 25A, the content model 2502 has several embeddedobjects fixed on specific locations on the content model. The objects inthis example are depicted as a camera 2508, a necklace 2510, and shoes2512. In some embodiments, visual indicators, such as glittering lights(depicted in FIG. 25A as a cluster of white dots), indicate the presenceof embedded objects. In some embodiments, the embedded objects are fixedto the content model 2502 and are affected by the user's manipulationsto content model 2502. For example, FIG. 25B shows that the contentmodel 2502 has been rotated by the user about 180 degrees. Similarly,the embedded objects also rotate with the content model. As shown inFIG. 25B, the embedded objects are no longer showing as accessible (noglittering lights) once they have rotated out of view. In someembodiments, the embedded objects are accessible regardless of the angleof rotation/view. However, in other embodiments, the embedded objectsare embedded in specific locations of content model 2502 and thus canonly be visible if content model 2502 is rotated in such a way as toexpose the embedded objects into view.

In some embodiments, the embedded objects are selectable by the user. Insuch embodiments, once selected, a pop-up window 2514 appears in thevirtual reality environment, as shown in FIGS. 25C-E. In someembodiments, the pop-up window 2514 depicts an enlarged view of theembedded object 2510. In some embodiments, the pop-up window produces asecond multi-view interactive digital media representation 2516 of theembedded object 2510. In other words, the pop-up window 2514 can also beanother virtual reality window/room where a content model 2516 of theembedded object 2510 can also be manipulated by the user. Suchembodiments may contain multiple surround view layers that make up thevirtual reality environment 2500. As shown in FIGS. 25D and 25E,multiple different embedded objects can be selected by the user and eachselection leads to another pop-up window 2514. In some embodiments, thepop-up windows 2514 are flat and fixed in the virtual reality space. Insuch embodiments, if the pop-up window 2514 is displayed and the userrotates the content model, the pop-up window also rotates with thecontent model. In other embodiments, window 2514 also provides amulti-view representation of the embedded object such that rotation ofthe content model leaves the window open, directly facing the user, butrotates the MIDMR of the embedded object inside the pop-up window.

In some embodiments, the user can use gloves with sensors, or a pointingdevice. In some embodiments, the user can use remote controls or mobiledevices to perform such actions. In some embodiments, constant motiondetectors surround the user of the virtual reality system and registerthe motions of the user without the user having to wear any devicesother than a head set, goggles, or the basic virtual reality engagementgear. As with virtual reality room 2400 in FIGS. 24A-C, virtual realityroom 2500 in FIGS. 25A-G can also be presented in AR format, where thebackground or “room” is the real world.

FIGS. 26A-M illustrate example views of a virtual reality environmentwith multiple interactive layers, in accordance with various embodimentsof the present disclosure. FIG. 26A shows an example model 2600. In thisexample the content model 2600 is a real estate model. The real estatecontent model 2600 constitutes multiple layers, including a first layerand also includes several embedded objects 2602 (Edmar Court), 2604 (511Edmar Avenue), and 2606 (623 Edmar Avenue) within the first layer. Asdemonstrated in FIG. 26B, both the real estate content model 2600 andthe embedded objects 2602, 2604, and 2606 can be viewed at differentangles through walking around the real estate content model 2600. InFIG. 26C, a selection device 2608 is used to select the embedded object2606 (623 Edmar Avenue), as shown in FIG. 26D. The selection of theembedded object causes a new window 2610 to appear, as shown in FIG.26E. The new window 2610 displays yet another content model 2612, thistime of a pink house. The pink house model 2612 is also a content modeland thus has multiple surround views as demonstrated by walking aroundthe real estate content model 2600 yet again, as shown in FIG. 26F. Thewindow 2610 containing the pink house model 2612 is a second layerembedded within the first layer of the real estate content model 2600.In FIG. 26G, the user moves the selection device one more time towardobject 2602 (Edmar Court) and selects object 2602 (as shown in FIG.26H). In FIG. 26H, window 2610 for object 2606 (623 Edmar Avenue) isreplaced by a window 2614 displaying Edmar circle 2616 because bothobject 2602 (Edmar Court) and object 2606 (623 Edmar Avenue) areembedded objects leading to second layer windows 2614 and 2610. In someembodiments, opening up a second layer window while another second layerwindow is open does not necessarily close/replace the already opensecond layer window. FIG. 26I demonstrates the user selecting object2604 (511 Edmar Avenue), which in turn pops up yet another window 2618displaying a white house content model 2620. As with window 2610corresponding to object 2606 (623 Edmar Avenue), window 2614 containingEdmar Court 2616 disappears because object 2604 (511 Edmar Avenue)produces a second layer window 2618. Because the content model 2620 inwindow 2618 has yet another embedded object, a green circle 2622 appearsto notify the user of the presence of the embedded object, shown in FIG.26J. In FIGS. 26K-L, the user uses the selection device 2608 to selectthe green dot/embedded object 2622 and a third window 2624 appears (FIG.26L). Since the third window 2624 is a third layer window correspondingto an embedded object 2622 located within the second layer, second layerwindow 2618 displaying model house 2620 (511 Edmar Avenue) does notdisappear. As demonstrated in FIG. 26M, the third layer window containsyet another content model 2626 (interior of 511 Edmar Avenue house),which itself is a panoramic style MIDMR. In some embodiments, theinterior content model 2626 is a convex type MIDMR while the othermodels (e.g., 2620) is a concave type MIDMR.

MIDMR Enhancement

In particular example embodiments, various algorithms can be employedduring capture of MIDM data, regardless of the type of capture modeemployed. These algorithms can be used to enhance MIDMRs and the userexperience. For instance, automatic frame selection, stabilization, viewinterpolation, image rotation, infinite smoothing, filters, and/orcompression can be used during capture of MIDM data. In some examples,these enhancement algorithms can be applied to image data afteracquisition of the data. In other examples, these enhancement algorithmscan be applied to image data during capture of MIDM data.

According to particular example embodiments, automatic frame selectioncan be used to create a more enjoyable MIDM view. Specifically, framesare automatically selected so that the transition between them will besmoother or more even. This automatic frame selection can incorporateblur- and overexposure-detection in some applications, as well as moreuniformly sampling poses such that they are more evenly distributed.

In some example embodiments, image stabilization can be used for MIDM ina manner similar to that used for video. In particular, keyframes in aMIDMR can be stabilized for to produce improvements such as smoothertransitions, improved/enhanced focus on the content, etc. However,unlike video, there are many additional sources of stabilization forMIDM, such as by using IMU information, depth information, computervision techniques, direct selection of an area to be stabilized, facedetection, and the like.

For instance, IMU information can be very helpful for stabilization. Inparticular, IMU information provides an estimate, although sometimes arough or noisy estimate, of the camera tremor that may occur duringimage capture. This estimate can be used to remove, cancel, and/orreduce the effects of such camera tremor.

In some examples, depth information, if available, can be used toprovide stabilization for MIDM. Because points of interest in a MIDMRare three-dimensional, rather than two-dimensional, these points ofinterest are more constrained and tracking/matching of these points issimplified as the search space reduces. Furthermore, descriptors forpoints of interest can use both color and depth information andtherefore, become more discriminative. In addition, automatic orsemi-automatic content selection can be easier to provide with depthinformation. For instance, when a user selects a particular pixel of animage, this selection can be expanded to fill the entire surface thattouches it. Furthermore, content can also be selected automatically byusing a foreground/background differentiation based on depth. In variousexamples, the content can stay relatively stable/visible even when thecontext changes.

According to various examples, computer vision techniques can also beused to provide stabilization for MIDM. For instance, keypoints can bedetected and tracked. However, in certain scenes, such as a dynamicscene or static scene with parallax, no simple warp exists that canstabilize everything. Consequently, there is a trade-off in whichcertain aspects of the scene receive more attention to stabilization andother aspects of the scene receive less attention. Because MIDM is oftenfocused on a particular object of interest, MIDM can be content-weightedso that the object of interest is maximally stabilized in some examples.

Another way to improve stabilization in MIDM includes direct selectionof a region of a screen. For instance, if a user taps to focus on aregion of a screen, then records a convex series of images, the areathat was tapped can be maximally stabilized. This allows stabilizationalgorithms to be focused on a particular area or object of interest.

In some examples, face detection can be used to provide stabilization.For instance, when recording with a front-facing camera, it is oftenlikely that the user is the object of interest in the scene. Thus, facedetection can be used to weight stabilization about that region. Whenface detection is precise enough, facial features themselves (such aseyes, nose, mouth) can be used as areas to stabilize, rather than usinggeneric keypoints.

According to various examples, view interpolation can be used to improvethe viewing experience. In particular, to avoid sudden “jumps” betweenstabilized frames, synthetic, intermediate views can be rendered on thefly. This can be informed by content-weighted keypoint tracks and IMUinformation as described above, as well as by denser pixel-to-pixelmatches. If depth information is available, fewer artifacts resultingfrom mismatched pixels may occur, thereby simplifying the process. Asdescribed above, view interpolation can be applied during capture ofMIDM in some embodiments. In other embodiments, view interpolation canbe applied during MIDM generation.

In some embodiments, IMU data such as tilt, direction, acceleration,etc. may be used to detect captured frames that are “out of line” ordeviating from a detected capture trajectory. For example, a 360 degreecapture of an object may be desired with a smooth concave trajectory.IMU may be used to predict a trajectory and can be used to discardframes or prevent capture of frames that are too far out of thepredicted trajectory beyond a certain threshold (or “out of line”threshold). For example, embodiments, if a sudden or rapid movement isdetected and associated with a captured frame, such captured frame maybe determined to be out of the trajectory line. As another example, suchtrajectory monitoring capability may eliminate a captured frame in whichthe object is too close or too far as compared to previously capturedframes along a trajectory. In various embodiments, the “out of line”threshold may be determined via a combination of x,y translation ofpixels and rotational movement of image frames in addition to the IMUdata. For example, position of keypoints in captured image frames may betracked over time in addition to the IMU data.

Such use of both translation and rotation are not implemented inexisting methods of image stabilization or interpolation. Additionally,existing methods of video stabilization use optical stabilization in thelens. This video stabilization, which occurs post-processing, includesshifting, but does not include scaling. Thus, larger frames are requiredbecause stabilization without scaling may cause the edge of each videoframe to be unaligned and unsmooth.

However, the methods and systems described herein may implement scalingfor stabilization of artificial frames interpolated between capturedframes. In one example embodiment, similarity 2D parameters, includingx,y translation, a 2D rotation, and a 2D scale, may be used to determinethe translation between frames. Such parameters may include 1 rotationvariable, 2 translation variables, and 2 scaling variables. By using acombination of translation, rotation, and scale, the methods and systemsdescribed herein is able to account for movement toward and away from anobject. In certain systems, if only keypoints are matched, then imagesmay be interpolated along a camera translation using a least squaresregression analysis. In other systems, keypoints may be matched using arandom sample consensus (RANSAC) algorithm as described further in thisdescription. Thus, the described methods and systems result in a set ofimages that have been stabilized along a smooth trajectory.

In some examples, view interpolation may be implemented as infinitesmoothing, which may also be used to improve the viewing experience bycreating a smoother transition between displayed frames, which may beactual or interpolated, as described above. Infinite smoothing mayinclude determining a predetermined amount of possible transformationsbetween frames. A Harris corner detector algorithm may be implemented todetect salient features to designate as keypoints in each frame, such asareas of large contrast, areas with minimum ambiguity in differentdimensions, and/or areas with high cornerness. A predetermined numberkeypoints with the highest Harris score may then be selected. a RANSAC(random sample consensus) algorithm may then be implemented to determinea number of the most common occurring transformations possible based onall possible transformations of the keypoints between frames. Forexample, a smooth flow space of eight possible transformations and/ormotions for various pixels between frames may be discretized. Differenttransformations may be assigned to different pixels in a frame. Suchkeypoint detection, keypoint tracking, and RANSAC algorithms may be runoffline. In some embodiments, infinite smoothing algorithms may be runin real time on the fly. For example, as the user navigate to aparticular translation position, and if that translation position doesnot already correspond to an existing and/or captured image frame, thesystem may generate an appropriate artificial image frame correspondingto the particular translation position using the optimal transformationchosen from the possible transformation candidates.

In various embodiments, infinite smoothing and other methods of viewinterpolation described herein may generate a smooth view around anobject or panoramic scene with fewer stored image frames. In someembodiments, a MIDMR may only require 10 or fewer stored image framesfrom which artificial frames may be interpolated. However in someembodiments, up to 100 stored image frames may be required. In yet otherembodiments, up to 1000 stored image frames may be required. The numberof stored image frames may depend on the angle range of cameratranslation. However, in such embodiments, the number of stored imageframes required for a given angle of camera translation is less with thesystem and methods described herein, than for conventional and existingmethods of image stitching. In some embodiments, up to 25 degrees of aconcave camera rotation around an object may be generated between twostored image frames with sufficient overlapping imagery. In someembodiments, even greater degrees of such camera rotation may begenerated from just two stored image frames. In various embodiments, theangle range of such camera rotation between two stored frames may dependupon the size of and amount of overlap in between the two stored frames.

According to various embodiments, MIDMRs provide numerous advantagesover traditional two-dimensional images or videos. Some of theseadvantages include: the ability to cope with moving scenery, a movingacquisition device, or both; the ability to model parts of the scene inthree-dimensions; the ability to remove unnecessary, redundantinformation and reduce the memory footprint of the output dataset; theability to distinguish between content and context; the ability to usethe distinction between content and context for improvements in theuser-experience; the ability to use the distinction between content andcontext for improvements in memory footprint (an example would be highquality compression of content and low quality compression of context);the ability to associate special feature descriptors with MIDM thatallow the MIDM to be indexed with a high degree of efficiency andaccuracy; and the ability of the user to interact and change theviewpoint of the MIDMR. In particular example embodiments, thecharacteristics described above can be incorporated natively in theMIDMR, and provide the capability for use in various applications. Forinstance, MIDM can be used to enhance various fields such as e-commerce,visual search, 3D printing, file sharing, user interaction, andentertainment.

Although MIDMR produced with described methods and systems may have somecharacteristics that are similar to other types of digital media such aspanoramas, according to various embodiments, MIDMRs include additionalfeatures that distinguish them from these existing types of digitalmedia. For instance, existing methods of generating panorama involvecombining multiple overlapping images together by matching similarand/or matching points and/or areas in each image and simply stitchingthe matching points and/or areas together. Overlapping areas arediscarded and the stitched image is then mapped to a sphere or cylinder.Thus such panoramas generated by existing methods have distorted edgesand lack parallax, causing scenes with foreground and background to lackan impression of depth and look unrealistic.

Furthermore, a stitched panorama comprises one large image afteroverlapping images are stitched. MIDMRs, as described herein, comprise aseries of images that are presented to the user as a user interacts withthe MIDMR or viewing device. The information in the overlaps of theseries of images, including interpolation information for generatingartificial frames in between captured frames, is stored. Matchingkeypoints are identified to compute intermediate frames and linearblending is implemented to transform an image between two captureframes. To compute intermediate frames, transformations are implemented,such as homography which may be used for stabilization, as well asscaling, which allows interpolated keypoints in images to match up. Nopart of any image frame is discarded. This causes parallax to be visiblein MIDMRs generated by systems and methods described herein, in contrastto existing panoramas,

Additionally, a MIDMR can represent moving data. Nor is a MIDMR is notlimited to a specific cylindrical, spherical or translational movement.Furthermore, unlike a stitched panorama, a MIDMR can display differentsides of the same object. Additionally, various motions can be used tocapture image data with a camera or other capture device.

Infinite Smoothing

In various embodiments, MIDMRs are enhanced using infinite smoothingtechniques. FIG. 27 illustrates an example method for infinite smoothingbetween image frames, in accordance with one or more embodiments. Withreference to FIG. 27 , shown is an example of a method 2700 for infinitesmoothing between image frames, in accordance with one or moreembodiments. In various embodiments, method 2700 may be implemented toparameterize a transformation, such as T_AB, for interpolation of thoseparameters during runtime.

At step 2701, first and second image frames are identified. In someembodiments, the first and second image frames may be part of a sequenceof images captured. In various embodiments, the image frames may beconsecutively captured images in time and/or space. In some embodiments,the first and second image frames may be adjacent image frames, such asframe N and frame N+1. The method 2700 described herein may beimplemented to render any number of frames between N and N+1 based onthe position of the user, user selection, and/or viewing device.

A random sample consensus (RANSAC) algorithm may be implemented todetermine the possible transformation candidates between the two imageframes. As described herein, transformation candidates may be identifiedfrom keypoints tracked from a first frame to a second frame. Varioustransformations may be calculated from various different parametersgathered from various combinations of keypoints. At step 2703, keypointsin the first frame and corresponding keypoints in the second frame areidentified. In some embodiments, the first frame includes an image thatwas captured before the image in the second frame. In other embodiments,the first frame may include an image captured after the image in thesecond frame. In various embodiments, keypoints may be identified usinga Harris-style corner detector algorithm or other keypoint detectionmethod. In other embodiments, various other corner detection algorithmsmay be implemented, such as a Moravec corner detection algorithm, aFörstner corner detector, etc. Such corner detector algorithm may beimplemented to detect salient features to designate as keypoints in eachframe, such as areas of large contrast, areas with minimum ambiguity indifferent dimensions, and/or areas with high cornerness. A predeterminednumber keypoints with the highest Harris score may then be selected. Forexample, 1,000 keypoints may be identified and selected on the firstframe. The corresponding 1,000 keypoints on the second frame can then beidentified using a Kanade-Lucas-Tomasi (KLT) feature tracker to trackkeypoints between the two image frames.

At step 2705, a transformation is determined for each correspondingkeypoint in each image frame. In some embodiments, a set of two keypointcorrespondences are used to determine a transformation. Variousparameters may be used to calculate the transformation betweencorresponding keyframes by a predetermined algorithm. In one exampleembodiment, similarity 2D parameters, including x,y translation, a 2Drotation, and a 2D scale, may be used to determine the translation.Other parameters that may be used include 2D translation (x and ytranslation), 2D Euclidean parameters (2D rotation and x,y translation),affine, homography, etc. The RANSAC algorithm may repeatedly selectcorresponding keyframes between image frames to determine thetransformation. In some embodiments, corresponding keyframes may beselected randomly. In other embodiments, corresponding keyframes may beselected by location.

Once all transformations have been calculated for each keyframecorrespondence, the most common occurring transformations are determinedas candidates at step 2707. According to various embodiments, keypointsmay be grouped based on the associated transformation calculated at step2705. In some embodiments, each transformation determined at step 2705is applied to all keypoints in an image, and the number of inlierkeypoints for which the transformation is successful is determined. Inother words, keypoints that experience the same transformation betweenthe first and second image frames are grouped together as inlierkeypoints. In some embodiments, a predetermined number oftransformations with the most associated inlier keypoints are selectedto be transformation candidates. In some embodiments, the imageintensity difference between a transformed image and the second imagemay also be calculated for each transformation determined at step 2705and applied to the keypoints. In some embodiments, image intensitydifference is only calculated if a transformation results in a largernumber of inlier keypoints than a previous determined transformation. Invarious embodiments, the transformations are ranked based on thecorresponding number of resulting inlier keypoints and/or imageintensity difference.

In various embodiments, a predetermined number of highest rankingtransformations are selected to be transformation candidates. In someembodiments, the remaining transformations determined at step 2705 arediscarded. Any number of transformation candidates may be selected.However, in some embodiments, the number of transformations selected astransformation candidates is a function of processing power. In someembodiments, processing time may increase linearly with increased numberof candidates. In an example embodiment, eight possible transformationcandidates with the most associated keypoints are selected. However, inother example embodiments, fewer than eight possible transformationcandidates may be selected to decrease required processing time ormemory. In some embodiments, steps 2703, 2705, and 2707 are run offline.In some embodiments, steps 2703, 2705, and 2707 are run in real-time, asimage frames are captured.

At step 2709, the optimal transformation candidate is applied to eachpixel. Each pixel in an image may experience a different transformationbetween frames. In some embodiments, each of the transformationcandidates is applied to each pixel. The transformation candidate thatresults in the least difference between frames may be selected. In someembodiments, each of the transformation candidates is applied to agroup, or “community,” of pixels. For example, a community of pixels maycomprise a 7×7 (−3, +3) group of pixels. Once an optimal transformationis applied to each pixel, an artificial image may be rendered at step2711. In various embodiments, steps 2709 and 2711 may be performedduring runtime when the user is viewing the sequence of images. In suchembodiments, the transformation may be a function of frame number of theframe between N and N+1. The number of frames between N and N+1 may bedetermined based on various considerations, such as the speed ofmovement and/or the distance between frames N and N+1. Because method2700 may generate any number of frames between frames N and N+1, theuser may perceive a smooth transition as the user view differentviewpoints of the three-dimensional model of an object of interest, asan image frame may be rendered for virtually any viewpoint position theuser is requesting to view. Furthermore, because the artificial imageframes may be rendered based on the calculated transformationparameters, storage of such artificial image frames is not required.This enhances the functioning of image processing computer systems byreducing storage requirements.

Method 2700 may then be implemented for the transition between eachimage frame in the sequence. Various embodiments of method 2700 mayprovide advantages over existing methods of rendering artificial images,such as alpha blending. Especially in the case of concave MIDMRs,existing methods result in artifacts or ghosting effect from improperlyaligned image frames. This occurs because unlike convex MIDMRs, concaveand/or flat MIDMRs do not experience a single transformation for allpixels and/or keypoints. Method 2700 provides a process for determiningthe optimal transformation out of multiple transformation candidates toapply to a pixel. Additionally, method 2700 may generate image framesthat are seen, as well as portions of image frames that are unseen.Thus, motion between two discretized image frames may be generated byselecting the frame that includes the least amount of conflict.

Stereoscopic Pairs for AR and VR

As described above, MIDMRs can be used in an AR or VR setting. FIG. 28illustrates a With reference to FIG. 28 , shown is an example method2800 for generating stereo pairs for virtual reality or augmentedreality using a single lens camera, in accordance with one or moreembodiments. At step 2801, a sequence of images is obtained. In someembodiments, the sequence of images may be multiple snapshots and/orvideo captured by a camera. In some embodiments, the camera may comprisea single lens for capturing sequential images one at a time. In someembodiments, the captured image may include 2D images, such as 2D images104. In some embodiments, other data may also be obtained from thecamera and/or user, including location information.

At step 2803, the sequence of images is fused to create a MIDMR. Forexample, the images and other data captured in step 2801 may be fusedtogether at a sensor fusion block. At step 2805, the captured contentand/or context is modeled. As previously described, the data that hasbeen fused together in step 2803 may then be used for content modelingand/or context modeling. As such, a MIDMR with a three-dimensional viewof an object and/or the context may be provided and accessed by a user.Various enhancement algorithms may be employed to enhance the userexperience. For instance, automatic frame selection, stabilization, viewinterpolation, image rotation, infinite smoothing, filters, and/orcompression can be used during capture of MIDM data. In some examples,these enhancement algorithms can be applied to image data afteracquisition of the data. In other examples, these enhancement algorithmscan be applied to image data during capture of MIDM data. In someembodiments, the enhancement algorithms may be applied during asubsequent step, such as at step 2811, described below.

At step 2807, a first frame is selected for viewing. In someembodiments, a first frame may be selected by receiving a request from auser to view an object of interest in a MIDMR. In some embodiments, therequest may also be a generic request to view a MIDMR without aparticular object of interest. In some embodiments, a particular firstframe may be specifically selected by the user. In some embodiments, thefirst frame may be designated for viewing by either the right eye or theleft eye. In the present example, the first frame selected at step 2807is designated for viewing by the left eye.

At step 2809, a second frame needed to create a stereo pair with thefirst frame is determined. The second frame may be designated forviewing by the other eye of the user, which is not designated to thefirst frame. Thus, in the present example, the second frame determinedat step 2809 is designated for viewing by the right eye. In variousembodiments, the second frame may be selected based on a desired angleof vergence at the object of interest and/or focal point. Vergencerefers to the simultaneous movement of both eyes in opposite directionsto obtain or maintain single binocular vision. When a creature withbinocular vision looks at an object, the each eye must rotate around avertical axis so that the projection of the image is in the center ofthe retina in both eyes. To look at an object closer by, the eyes rotatetowards each other (convergence), while for an object farther away theyrotate away from each other (divergence). Exaggerated convergence iscalled cross eyed viewing (focusing on one's nose for example). Whenlooking into the distance, the eyes diverge until parallel, effectivelyfixating the same point at infinity (or very far away). As used herein,the angle of vergence refers to the angle between the lines of sight ofeach frame to the object of interest and/or desired focal point. In someembodiments, a degree of vergence may be between 5 degrees to 10degrees. In some embodiments, a desired degree of vergence of more than10 degrees may cause a user to see different objects and/or experiencedisjointed views (i.e., double vision or diplopia).

In some embodiments, the second frame may additionally be selected basedon gathered location and/or IMU information. For example, if the objectof interest and/or focal point is closer, a larger degree of vergencemay be desired to convey an appropriate level of depth. Conversely, ifthe object of interest and/or focal point is further away, a smallerdegree of vergence may be desired.

In some embodiments, the degree of vergence may then be used todetermine a spatial baseline. The spatial baseline refers to thedistance between the left eye and the right eye, and consequently, thedistance between the first frame and the second frame. The averagedistance between the left eye and right eye of a human is about 10 cm to15 cm. However, in some embodiments, a wider spatial baseline may beallowed in order to enhance the experience effect of depth. For example,a desired spatial baseline may be 30 cm.

Once the distance of the spatial baseline has been determined, a secondframe located at that distance away from the first frame may be selectedto be used as the stereo pair of the first frame. In some embodiments,the second frame located at the determined distance may be an actualframe captured by the camera at step 2801. In some embodiments, thesecond frame located at the determined distance may be an artificialframe generated by interpolation, or other enhancement algorithms, increating the MIDMR. In other embodiments, an artificial second frame maybe generated by various enhancement algorithms described below withreference to step 2809.

At step 2811, enhancement algorithms are applied to the frames. In someembodiments, enhancement algorithms may only be applied to the secondframe. In some embodiments, step 2811 may alternatively, oradditionally, occur after step 2805 and before selecting the first framefor viewing at step 2807. In various embodiments, such algorithms mayinclude: automatic frame selection, stabilization, view interpolation,filters, and/or compression. In some embodiments, the enhancementalgorithms may include image rotation. In order for the user to perceivedepth, the view of each frame must be angled toward the object ofinterest such that the line of sight to the object of interest isperpendicular to the image frame. In some embodiments, certain portionsof the image of a frame may be rotated more or less than other portionsof that image. For example, portions identified as context and/orbackground with a focal point at infinity may be rotated less than anearby object of interest in the foreground identified as the content.

In some embodiments, image rotation may include using IMU and image datato identify regions that belong to the foreground and regions thatbelong to the background. For example, rotation information from the IMUdata informs how a keypoint at infinity should move. This then can beused to identify foreground regions where a keypoint's movement violatesthe optical flow for infinity. In some embodiments, the foreground maycorrespond to the content or an object of interest, and the backgroundmay correspond to the context. In some embodiments, the keypoints may beused to determine optimal transformation for one or more images in astereo pair. In some embodiments, the keypoints are used to determinefocal length and rotation parameters for the optimal transformation.

A Harris corner detector algorithm may be implemented to detect salientfeatures to designate as keypoints in each frame, such as areas of largecontrast, areas with minimum ambiguity in different dimensions, and/orareas with high cornerness. In some embodiments, only keypointscorresponding to the object of interest and/or content are designated.For example, when performing image rotation for a concave MIDMR, onlykeypoints corresponding to the object of interest and/or content will bedesignated and used. However, where image rotation is used for a convexMIDMR, keypoints corresponding to both the background and the foregroundmay be designated and used. Then, a Kanade-Lucas-Tomasi (KLT) featuretracker may be used to track keypoints between two image frames. In someembodiments, one or more keypoints tracked by the KLT feature trackerfor image rotation may be the same keypoints used by other enhancementalgorithms, such as infinite smoothing and/or view interpolation, asfurther described herein.

Two keypoints in a first frame and corresponding keypoints in a secondframe may be selected at random to determine the rotationtransformation. Based on the two keypoint correspondences, the focallength and rotation are solved to calculate the transformation. Invarious embodiments, only keypoints corresponding to the foregroundregions are used to solve for focal length and rotation. In someembodiments, finding the optimal rotation transformation may furtherinclude minimizing the image intensity difference between the foregroundregions of the two image frames. This two-dimensional 3×3 imagetransformation can be mapped from the combination of an actual 3D camerarotation and the focal length. The new pre-rotated image sequence isthen produced given the solved transformation.

In some embodiments, a frame that is located at a particular point alongthe camera translation, which needed to create a stereo pair, may notexist. An artificially frame may be rendered to serve as the framerequired to complete the stereo pair. Accordingly, by generating theseartificially rendered frames, smooth navigation within the MIDMR becomespossible. In some embodiments, frames that have been rotated based onmethods described with respect to step 2811 are already stabilized andcorrectly focused onto the object of interest. Thus, image framesinterpolated based on these rotated frames may not require additionalimage rotation applied.

At step 2813, the stereo pair is presented to the user. In someembodiments, a first frame in the stereo pair is designated to be viewedby the user's left eye, while the second frame is designated to beviewed by the user's right eye. In some embodiments, the first andsecond frames are presented to the respective eye each frame isdesignated for, such that only the left eye views the first frame whileonly the right eye views the second frame. For example, the frames maybe presented to the user in a viewing device, such as a virtual realityheadset. This effectively applies a 3×3 image warp to the left eye andright eye images. By viewing each frame in the stereo pair with separateeyes in this way, these two-dimensional images are combined in theuser's brain to give the perception of 3D depth.

The method may then return to step 2807 to select another frame forviewing. As previously described above, a subsequent frame may beselected by the user. In other embodiments, a subsequent frame may beselected based on a received user action to view the object of interestfrom a second viewpoint. For example, this user action can includemoving (e.g. tilting, translating, rotating, etc.) an input device,swiping the screen, etc., depending on the application. For instance,the user action can correspond to motion associated with a locallyconcave MIDMR, a locally convex MIDMR, or a locally flat MIDMR, etc.Additionally, the user action may include movement of the user and/or aviewing device in three-dimensional space. For example, if the usermoves the viewing device to another location in three-dimensional space,an appropriate frame corresponding to the view of the object ofinterest, content, and/or context from that camera location in threedimensional space. As previously described, intermediate images can berendered between image frames in a MIDMR. Such intermediate imagescorrespond to viewpoints located between the viewpoints of the existingimage frames. In some embodiments, stereo pairs may be generated foreach of these intermediate images and presented to the user by method2800.

Thus, method 2800 may be used to generate stereoscopic pairs of imagesfor a monocular image sequence captured by a single lens camera. Unlikeexisting methods in which stereoscopic pairs are created bysimultaneously capturing two images at a predetermined distance apartalong a camera translation, method 2800, and other processes describedherein, can create stereoscopic pairs with only a sequence of singleimages captured along a camera translation. Thus, fewer images, andcorresponding image data is required, resulting in less data storage.Moreover, the information required for selection of stereoscopic pairsand image rotation for method 2800 do not need to be stored and may bedetermined in real-time. Additionally, parameters are not set forstereoscopic pairs of images generated by method 2800, unlike inexisting methods. For example, a wider or shorter distance may beselected between each image frame in a stereoscopic pair in order toincrease or decrease the depth perception, respectively. Furthermore,one or more various objects within an image sequence may be determinedto be an object of interest and different rotation. Images may berotated differently depending on which object or objects are determinedto be the object of interest. Moreover, various portions within an imagemay be rotated differently based on the determined object of interest.In other words, different rotation transformations may be determined fordifferent portions of an image.

By generating and presenting stereo pairs corresponding to sequence ofimage frames in a MIDMR, method 2800 may be used to provide depth to theMIDMR. In various instances, this allows the user to perceive depth in ascene and/or an object of interest presented as a three-dimensionalmodel without actually rendering and/or storing an actualthree-dimensional model. In other words, there is no polygon generationor texture mapping over a three-dimensional mesh and/or polygon model,as in existing methods. However, the user still perceives the contentand/or context as an actual three-dimensional model with depth frommultiple viewpoint angles. The three-dimensional effect provided by theMIDMR is generated simply through stitching of actual two-dimensionalimages and/or portions thereof, and generation of stereo pairscorresponding to the two-dimensional images.

Although many of the components and processes are described above in thesingular for convenience, it will be appreciated by one of skill in theart that multiple components and repeated processes can also be used topractice the techniques of the present disclosure.

While the present disclosure has been particularly shown and describedwith reference to specific embodiments thereof, it will be understood bythose skilled in the art that changes in the form and details of thedisclosed embodiments may be made without departing from the spirit orscope of the disclosure. It is therefore intended that the disclosure beinterpreted to include all variations and equivalents that fall withinthe true spirit and scope of the present disclosure.

What is claimed is:
 1. A method for generating a multi-view interactivedigital media representation in a virtual reality environmentcomprising: obtaining a plurality of images, wherein the plurality ofimages include at least a portion of overlapping subject matter; fusing,via a processor, the plurality of images into a content model, whereinthe content model includes a multi-view interactive digital mediarepresentation of the object; and generating, via the processor, avirtual reality environment using the content model; wherein themulti-view interactive digital media representation is generated byfusing each object in the plurality of images into a single contentmodel object without using background in the plurality of images,connected together in a three-dimensional spatial graph, wherein thecontent model is generated directly from the plurality of images withoutusing a 3D polygon model, wherein the content model includes multipleimages captured from different perspectives around the object; whereinthe multi-view interactive digital media representation is configuredsuch that a user can navigate through and within the virtual realityenvironment to switch between multiple viewpoints of the content modelvia corresponding physical movements, wherein the user can directlyrotate the content model while the user is in the virtual realityenvironment against a static context model background and walk aroundthe object while the user is in the virtual reality environment, whereinwalking around the object includes rotating the content model inconjunction with a rotating context model background; associating a tagwith a region in the content model, wherein region and the associatedtag is automatically propagated into a plurality of views of the contentmodel.
 2. The method of claim 1, wherein the plurality of images isobtained from a plurality of users.
 3. The method of claim 1, whereinthe virtual reality environment is enhanced using one or more ofautomatic frame selection, stabilization, view interpolation, filters,and compression.
 4. The method of claim 1, wherein the plurality ofimages includes images with different temporal information.
 5. Themethod of claim 1, wherein the static context model background includesa locally concave surround view, surround view surrounding the contentmodel in a 360 degree navigable environment.
 6. The method of claim 1,wherein the content model includes a locally convex surround view of theobject.
 7. The method of claim 1, wherein the virtual realityenvironment is configured such that the user can appear to be closer orfarther to surround views by physically moving through space.
 8. Asystem for generating a multi-view interactive digital mediarepresentation in a virtual reality environment comprising: obtaining aplurality of images, wherein the plurality of images include at least aportion of overlapping subject matter; fusing, via a processor, theplurality of images into a content model, wherein the content modelincludes a multi-view interactive digital media representation of theobject; and generating, via the processor, a virtual reality environmentusing the content model; wherein the multi-view interactive digitalmedia representation is generated by fusing each object in the pluralityof images into a single content model object without using background inthe plurality of images, connected together in a three-dimensionalspatial graph, wherein the content model is generated directly from theplurality of images without using a 3D polygon model, wherein thecontent model includes multiple images captured from differentperspectives around the object; wherein the multi-view interactivedigital media representation is configured such that a user can navigatethrough and within the virtual reality environment to switch betweenmultiple viewpoints of the content model via corresponding physicalmovements, wherein the user can directly rotate the content model whilethe user is in the virtual reality environment against a static contextmodel background and walk around the object while the user is in thevirtual reality environment, wherein walking around the object includesrotating the content model in conjunction with a rotating context modelbackground; associating a tag with a region in the content model,wherein region and the associated tag is automatically propagated into aplurality of views of the content model.
 9. The system of claim 8,wherein the plurality of images is obtained from a plurality of users.10. The system of claim 8, wherein the virtual reality environment isenhanced using one or more of automatic frame selection, stabilization,view interpolation, filters, and compression.
 11. The system of claim 8,wherein the plurality of images includes images with different temporalinformation.
 12. The system of claim 8, wherein the static context modelbackground includes a locally concave surround view, the surround viewsurrounding the content model in a 360 degree navigable environment. 13.The system of claim 8, wherein the content model includes a locallyconvex surround view of the object.
 14. The system of claim 8, whereinthe virtual reality environment is configured such that the user canappear to be closer or farther to surround views by physically movingthrough space.
 15. A non-transitory computer readable medium comprisinginstructions to execute a method for generating a multi-view interactivedigital media representation in a virtual reality environment, themethod comprising: obtaining a plurality of images, wherein theplurality of images include at least a portion of overlapping subjectmatter; fusing, via a processor, the plurality of images into a contentmodel, wherein the content model includes a multi-view interactivedigital media representation of the object; and generating, via theprocessor, a virtual reality environment using the content model;wherein the multi-view interactive digital media representation isgenerated by fusing each object in the plurality of images into a singlecontent model object without using background in the plurality ofimages, connected together in a three-dimensional spatial graph, whereinthe content model is generated directly from the plurality of imageswithout using a 3D polygon model, wherein the content model includesmultiple images captured from different perspectives around the object;wherein the multi-view interactive digital media representation isconfigured such that a user can navigate through and within the virtualreality environment to switch between multiple viewpoints of the contentmodel via corresponding physical movements, wherein the user candirectly rotate the content model while the user is in the virtualreality environment against a static context model background and walkaround the object while the user is in the virtual reality environment,wherein walking around the object includes rotating the content model inconjunction with a rotating context model background; associating a tagwith a region in the content model, wherein region and the associatedtag is automatically propagated into a plurality of views of the contentmodel.
 16. The non-transitory computer readable medium of claim 15,wherein the virtual reality environment is enhanced using one or more ofautomatic frame selection, stabilization, view interpolation, filters,and compression.
 17. The non-transitory computer readable medium ofclaim 15, wherein the plurality of images includes images with differenttemporal information.
 18. The non-transitory computer readable medium ofclaim 15, wherein the static context model background includes a locallyconcave surround view, the surround view surrounding the content modelin a 360 degree navigable environment.
 19. The non-transitory computerreadable medium of claim 15, wherein the content model includes alocally convex surround view of the object.
 20. The non-transitorycomputer readable medium of claim 15, wherein the virtual realityenvironment is configured such that the user can appear to be closer orfarther to surround views by physically moving through space.